Systems and methods for controlling variable refrigerant flow systems and equipment using artificial intelligence models

ABSTRACT

An oil management controller for heating, ventilation, or air conditioning (HVAC) equipment. The controller includes a processing circuit. The processing circuit is configured to analyze operating data for the HVAC equipment using a machine learning model to predict a variable state or condition of oil used by the HVAC equipment. The processing circuit is configured to identify an oil deficiency based on the variable state or condition of the oil. The processing circuit is configured to automatically initiate a corrective action responsive to identifying the oil deficiency.

BACKGROUND

The present disclosure relates generally to the field of operatingbuilding equipment and more particularly to using artificialintelligence to predict states of the building equipment.

For building equipment (e.g., heating, ventilation, or air conditioning(HVAC) equipment) to operate effectively and to minimize degradation ofthe building equipment, various operating conditions of the buildingequipment should be monitored and accounted for. However, traditionalbuilding systems leave many operating conditions unmonitored which canlead to rapid degradation of the building equipment and increased costsover time.

SUMMARY

One embodiment of the present disclosure is an oil management controllerfor heating, ventilation, or air conditioning (HVAC) equipment. Thecontroller includes a processing circuit. The processing circuit isconfigured to analyze operating data for the HVAC equipment using amachine learning model to predict a variable state or condition of oilused by the HVAC equipment. The processing circuit is configured toidentify an oil deficiency based on the variable state or condition ofthe oil. The processing circuit is configured to automatically initiatea corrective action responsive to identifying the oil deficiency.

In some embodiments, the variable state or condition of the oil is anamount of the oil in the HVAC equipment. Identifying the oil deficiencyincludes determining that the amount of the oil in the HVAC equipment isless than a threshold amount. The corrective action includes providingmore oil to the HVAC equipment to increase the amount of the oil in theHVAC equipment.

In some embodiments, the variable state or condition of the oil is aviscosity of an oil-refrigerant mixture in the HVAC equipment.Identifying the oil deficiency includes determining that the viscosityof the oil-refrigerant mixture is less than a threshold viscosity. Thecorrective action includes providing more oil to the HVAC equipment toincrease the viscosity of the oil-refrigerant mixture in the HVACequipment.

In some embodiments, the HVAC equipment is operable at differentoperating speeds. The variable state or condition of the oil is aviscosity of an oil-refrigerant mixture in the HVAC equipment. Thecorrective action includes setting an upper limit on an operating speedof the HVAC equipment based on the viscosity of the oil-refrigerantmixture.

In some embodiments, the HVAC equipment is coupled to a refrigerant loopthat circulates an oil-refrigerant mixture between the HVAC equipmentand one or more other devices coupled to the refrigerant loop. Thecorrective action includes operating the HVAC equipment to cause theoil-refrigerant mixture to circulate within the refrigerant loop andthereby return the oil from the one or more other devices to the HVACequipment.

In some embodiments, the machine learning model is a convolutionalneural network (CNN) model having an input layer, one or more hiddenlayers, and an output layer. Analyzing the operating data includesproviding the operating data as inputs to the input layer of the CNNmodel and obtaining a prediction of the variable state or condition ofthe oil at the output layer of the CNN model.

In some embodiments, the machine learning model is a recurrent neuralnetwork (RNN) model. Analyzing the operating data includes providing atime series of values of the operating data as an input to the RNN modeland obtaining a prediction of the variable state or condition of the oilas an output of the RNN model.

In some embodiments, the processing circuit configured to generate themachine learning model using a set of training data obtained from asimulation model.

Another embodiment of the present disclosure is a method for operatingheating, ventilation, or air conditioning (HVAC) equipment using amachine learning model. The method includes obtaining training dataindicating conditions affecting oil used by the HVAC equipment and avariable state or condition of the oil. The method includes generatingthe machine learning model by performing a training process based on thetraining data. The machine learning model is trained to predict thevariable state or condition of the oil based on the conditions affectingthe oil. The method includes using the machine learning model to predictwhether the variable state or condition of the oil violates a threshold.The method includes automatically initiating a corrective actionresponsive to predicting that the variable state or condition of the oilviolates the threshold.

In some embodiments, the variable state or condition of the oil is anamount of the oil in the HVAC equipment. The threshold is a minimumthreshold for the amount of the oil in the HVAC equipment. Thecorrective action includes providing more oil to the HVAC equipment toincrease the amount of the oil in the HVAC equipment.

In some embodiments, the variable state or condition of the oil is aviscosity of an oil-refrigerant mixture in the HVAC equipment. Thethreshold is a minimum threshold for the viscosity of theoil-refrigerant mixture. The corrective action includes providing moreoil to the HVAC equipment to increase the viscosity of theoil-refrigerant mixture in the HVAC equipment.

In some embodiments, the HVAC equipment is operable at differentoperating speeds. The variable state or condition of the oil is aviscosity of an oil-refrigerant mixture in the HVAC equipment. Thecorrective action includes setting an upper limit on an operating speedof the HVAC equipment based on the viscosity of the oil-refrigerantmixture.

In some embodiments, the HVAC equipment is coupled to a refrigerant loopthat circulates an oil-refrigerant mixture between the HVAC equipmentand one or more other devices coupled to the refrigerant loop. Thecorrective action includes operating the HVAC equipment to cause theoil-refrigerant mixture to circulate within the refrigerant loop andthereby return the oil from the one or more other devices to the HVACequipment.

In some embodiments, the machine learning model is a convolutionalneural network (CNN) model having an input layer, one or more hiddenlayers, and an output layer. Using the machine learning model todetermine if the variable state or condition of the oil violates theconstraint includes providing operating data as inputs to the inputlayer of the CNN model and obtaining a prediction of the variable stateor condition of the oil at the output layer of the CNN model.

In some embodiments, the machine learning model is a recurrent neuralnetwork (RNN) model. Using the machine learning model to determine ifthe variable state or condition of the oil violates the constraintincludes providing a time series of values of operating data as an inputto the RNN model and obtaining a prediction of the variable state orcondition of the oil as an output of the RNN model.

In some embodiments, obtaining the training data includes obtaining asimulation model that simulates operation of the HVAC equipment andchanges to the variable state or condition of the oil over time.Obtaining the training data includes executing the simulation model togenerate the training data.

Another embodiment of the present disclosure is an environmental controlsystem for a building. The system includes heating, ventilation, or airconditioning (HVAC) equipment operable to affect an environmentalcondition of the building. The system includes a controller including aprocessing circuit. The processing circuit is configured to analyzeoperating data for the HVAC equipment using a machine learning model topredict a variable state or condition of oil used by the HVAC equipment.The processing circuit is configured to identify an oil deficiency basedon the variable state or condition of the oil. The processing circuit isconfigured to automatically initiate a corrective action responsive toidentifying the oil deficiency.

In some embodiments, the variable state or condition of the oil is anamount of the oil in the HVAC equipment. Identifying the oil deficiencyincludes determining that the amount of the oil in the HVAC equipment isless than a threshold amount. The corrective action includes providingmore oil to the HVAC equipment to increase the amount of the oil in theHVAC equipment.

In some embodiments, the variable state or condition of the oil is aviscosity of an oil-refrigerant mixture in the HVAC equipment.Identifying the oil deficiency includes determining that the viscosityof the oil-refrigerant mixture is less than a threshold viscosity. Thecorrective action includes providing more oil to the HVAC equipment toincrease the viscosity of the oil-refrigerant mixture in the HVACequipment.

In some embodiments, the HVAC equipment is operable at differentoperating speeds. The variable state or condition of the oil is aviscosity of an oil-refrigerant mixture in the HVAC equipment. Thecorrective action includes setting an upper limit on an operating speedof the HVAC equipment based on the viscosity of the oil-refrigerantmixture.

Another embodiment of the present disclosure is a controller foroperating a motor of a compressor in a heating, ventilation, or airconditioning (HVAC) system, according to some embodiments. The controlincludes a processing circuit. The processing circuit is configured toobtain a machine learning model that predicts amplitude setpoints for anelectric current provided to the motor. The amplitude setpoints affectvibrations of the motor. The processing circuit is configured to analyzeoperating data for the motor using the machine learning model to predictan amplitude setpoint for the electric current. The processing circuitis configured to operate the motor based on the amplitude setpoint.

In some embodiments, operating the motor based on the amplitude setpointincludes providing the amplitude setpoint to an inverter. The inverteris configured to provide the electric current to the motor.

In some embodiments, inputs to the machine learning model include atleast one of an axial error between a direct axis and a quadrature axisof the motor, a frequency of the electric current, a real noise levelassociated with the motor, or a required noise level associated with themotor.

In some embodiments, the machine learning model is trained to learn acorrelation between the operating data and an amount of noise producedby the motor. The amount of noise is used as a proxy for predicting thevibrations of the motor.

In some embodiments, the processing circuit further configured togenerate the machine learning model using a set of training dataobtained from a simulation model.

some embodiments, the machine learning model is a recurrent neuralnetwork (RNN) model. Analyzing the operating data includes providing atime series of values of the operating data as an input to the RNN modeland obtaining a prediction of the amplitude setpoint as an output of theRNN model.

In some embodiments, the electric current is an alternating current(AC). A frequency of the AC affects a rotational speed of the motor andan amplitude of the AC affects a torque applied by the motor.

Another embodiment of the present disclosure is a controller forpredicting faults of a variable refrigerant flow (VRF) system. Thecontroller includes a processing circuit. The processing circuit isconfigured to analyze operating data for the VRF system using a machinelearning model to predict a fault classification for the VRF system. Thefault classification identifies a fault condition affecting the VRFsystem. The processing circuit is configured to identify a VRF device ofthe VRF system associated with the fault condition. The processingcircuit is configured to automatically initiate a corrective action toaddress the fault condition responsive to identifying the device and thefault condition.

In some embodiments, the fault classification includes a severity metricassociated with the fault condition. The severity metric indicates aninfluence of the fault condition on the VRF system. The correctiveaction is determined based on the severity metric.

In some embodiments, the machine learning model is a recurrent neuralnetwork (RNN) model. Analyzing the operating data includes providing atime series of values of the operating data as an input to the RNN modeland obtaining a prediction of the fault classification as an output ofthe RNN model.

In some embodiments, the processing circuit further configured togenerate the machine learning model using a set of training dataobtained from a simulation model.

In some embodiments, the fault classification identifies multiple faultconditions affecting the VRF system. The multiple fault conditions areassociated with multiple VRF devices of the VRF system.

In some embodiments, the fault condition is at least one of leakage of arefrigerant, frosting of an outdoor unit, clogging of an indoor fan,clogging of an indoor filter, clogging of a heat exchanger, clogging ofan outdoor fan, demagnetization of a motor, or leakage of oil from acompressor.

Another embodiment of the present disclosure is a method for operating amotor of a compressor in a heating, ventilation, or air conditioning(HVAC) system, according to some embodiments. The method includesobtaining a machine learning model that predicts amplitude setpoints foran electric current provided to the motor. The amplitude setpointsaffect vibrations of the motor. The method includes analyzing operatingdata for the motor using the machine learning model to predict anamplitude setpoint for the electric current. The method includesoperating the motor based on the amplitude setpoint.

In some embodiments, operating the motor based on the amplitude setpointincludes providing the amplitude setpoint to an inverter. The inverteris configured to provide the electric current to the motor.

In some embodiments, inputs to the machine learning model include atleast one of an axial error between a direct axis and a quadrature axisof the motor, a frequency of the electric current, a real noise levelassociated with the motor, or a required noise level associated with themotor.

In some embodiments, the machine learning model is trained to learn acorrelation between the operating data and an amount of noise producedby the motor. The amount of noise is used as a proxy for predicting thevibrations of the motor.

In some embodiments, the method includes generating the machine learningmodel using a set of training data obtained from a simulation model.

In some embodiments, the machine learning model is a recurrent neuralnetwork (RNN) model. Analyzing the operating data includes providing atime series of values of the operating data as an input to the RNN modeland obtaining a prediction of the amplitude setpoint as an output of theRNN model.

In some embodiments, the electric current is an alternating current(AC). A frequency of the AC affects a rotational speed of the motor andan amplitude of the AC affects a torque applied by the motor.

Those skilled in the art will appreciate that the summary isillustrative only and is not intended to be in any way limiting. Otheraspects, inventive features, and advantages of the devices and/orprocesses described herein, as defined solely by the claims, will becomeapparent in the detailed description set forth herein and taken inconjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Various objects, aspects, features, and advantages of the disclosurewill become more apparent and better understood by referring to thedetailed description taken in conjunction with the accompanyingdrawings, in which like reference characters identify correspondingelements throughout. In the drawings, like reference numbers generallyindicate identical, functionally similar, and/or structurally similarelements.

FIG. 1 is a drawing of a building equipped with a HVAC system, accordingto some embodiments.

FIG. 2 is a block diagram of a waterside system which can be used toserve the heating or cooling loads of the building of FIG. 1 , accordingto some embodiments.

FIG. 3 is a block diagram of an airside system which can be used toserve the heating or cooling loads of the building of FIG. 1 , accordingto some embodiments.

FIG. 4 is a block diagram of a building management system (BMS) whichcan be used to monitor and control the building of FIG. 1 , according tosome embodiments.

FIG. 5 is a block diagram of another BMS which can be used to monitorand control the building of FIG. 1 , according to some embodiments.

FIGS. 6A-6B are drawings of a variable refrigerant flow (VRF) systemhaving one or more outdoor VRF units and multiple indoor VRF units,according to some embodiments.

FIG. 7A is a schematic diagram of a VRF system, according to someembodiments.

FIG. 7B is a block diagram of a VRF system, according to someembodiments.

FIG. 8 is a block diagram of a controller for predicting characteristicsof oil, according to some embodiments.

FIG. 9A is an illustration of a recurrent neural network (RNN)structure, according to some embodiments.

FIG. 9B is an illustration of a neural network (NN) architecture,according to some embodiments.

FIG. 10 is a flow diagram of a process for monitoring oilcharacteristics using an AI model, according to some embodiments.

FIG. 11A is a graph illustrating changes in RMSE based on a number ofiterations in an example model training process for an artificialintelligence (AI) model, according to some embodiments.

FIG. 11B is a graph illustrating changes in loss based on the number ofiterations associated with the AI model of FIG. 11A, according to someembodiments.

FIG. 12A is a graph illustrating predictions of an oil level of acompressor generated by the AI model of FIG. 11A, according to someembodiments.

FIG. 12B is a graph illustrating predictions of an oil level of anaccumulator generated by the AI model of FIG. 11A, according to someembodiments.

FIG. 12C is a graph illustrating predictions of a viscosity of oilgenerated by the AI model of FIG. 11A, according to some embodiments.

FIG. 13A is a block diagram of a VRF system including a compressorvibration controller, according to some embodiments.

FIG. 13B is a block diagram of the VRF system of FIG. 13A in greaterdetail, according to some embodiments.

FIG. 14A is a graph illustrating Kirchhoff's law, according to someembodiments.

FIG. 14B is a graph illustrating an αβ conversion for a motor, accordingto some embodiments.

FIG. 14C is a graph illustrating a direct-quadrature (dq) conversion fora motor, according to some embodiments.

FIG. 15 is a graph illustrating a relationship between crank angle andtorque for different types of compressors, according to some embodiments

FIG. 16 is a block diagram of the compressor vibration controller ofFIG. 13A in greater detail, according to some embodiments.

FIG. 17A is an illustration of a neural network for predicting values ofa current to provide to a compressor motor, according to someembodiments.

FIG. 17B is an illustration of another neural network for predictingvalues of a current to provide to a compressor motor, according to someembodiments.

FIG. 18 is a flow diagram of a process for predicting an AC signalamplitude to provide to a compressor using an AI model, according tosome embodiments.

FIG. 19 is a block diagram of a VRF system, according to someembodiments.

FIG. 20 is a block diagram of a VRF fault controller for predictingfaults in a VRF system, according to some embodiments.

FIG. 21 is an illustration of a neural network for predicting a faultclassification of a VRF system, according to some embodiments.

FIG. 22 is a flow diagram of a process for predicting a faultclassification for a VRF system using an AI model, according to someembodiments.

FIG. 23 is a block diagram of a motor efficiency controller, accordingto some embodiments.

FIG. 24 is a flow diagram of a process for predicting an efficiency of amotor in a VRF system using an AI model, according to some embodiments.

FIG. 25A is an illustration of a recurrent neural network structure,according to some embodiments.

FIG. 25B is an illustration of a long short-term memory model structure,according to some embodiments.

DETAILED DESCRIPTION Overview

Referring generally to the FIGURES, systems and methods for utilizingartificial intelligence (AI) in predicting characteristics of variablerefrigerant flow (VRF) systems of a building and operating VRF systemsand VRF system components are shown, according to some embodiments. Inparticular, the present disclosure utilizes AI to predictcharacteristics of oil used in VRF systems as well as for predictingvarious states and characteristics of motors and compressors in VRFsystems.

It should be appreciated, however, that the systems and methodsdescribed herein are not limited to VRF systems. Rather, VRF systems areshown and described for sake of example only as one potentialimplementation of the present disclosure. The systems and methodsdescribed herein can be applied to a variety of systems (e.g., otherenvironmental control systems) that require oil to be provided toequipment, as well as other types of systems that include compressors,motors, any type of equipment that uses oil, and/or any type ofequipment that may experience vibration or faults during operation. Forexample, the systems and methods described herein can be applied to avariety of heating, ventilation, or air conditioning (HVAC) systems anddevices (e.g., various air conditioning equipment, variable air volume(VAV) systems, residential air conditioning (RAC) systems, etc.).

As referred to herein, AI and AI models can be used to describe avariety of different models that can be used in predicting states andother information associated with devices in VRF systems. In someembodiments, recurrent neural network (RNN) models are utilized forgenerating predictions. RNNs are a class of artificial neural networkswhere connections between nodes form a directed graph along a temporalsequence. More specifically, long short-term memory (LSTM) models may beutilized in generating predictions. LSTMs are a specific type ofartificial RNN architecture that are used primarily for deep learning.LSTMs can classify and process entire sequences of time-series data andcan make predictions based on said time-series data. Advantageously,LSTMs can account for lags of unknown duration between important eventsin a time series. In some embodiments, other types of AI models such asconvolutional neural networks (CNNs) are utilized in generatingpredictions. Accordingly, it should be appreciated that various types ofAI models can be utilized in generating predictions.

As defined herein, a characteristic of oil, which is usedinterchangeably herein with the term “oil characteristic,” can refer toa particular property of the oil. In other words, an oil characteristicmay be a variable state or condition of the oil. Oil characteristics(i.e., variable states or conditions of the oil) of a VRF system mayinclude, for example, oil levels in one or more compressors of the VRFsystem, an oil level in an accumulator of the VRF system, a viscosity ofthe oil, etc. In some embodiments, a viscosity of an oil-refrigerantmixture is estimated instead of or in addition to the viscosity purelyof the oil. In this case, the oil-refrigerant mixture may be outputtedby compressors of the VRF system as a result of oil getting integratedinto compressed refrigerant typically outputted by the compressors. Asthe VRF system is operated, the characteristics of the oil may changeover time, thereby leading to changes in operation of building devices(e.g., compressors, valves, oil separators, etc.) in the VRF system. Forexample, if a compressor using the oil operates at a higher speed, alevel of oil in the compressor may decrease and a viscosity of the oilmay decrease due to a higher operating temperature of the compressor.

Specifically with regard to a VRF system, traditional time-based oilreturn systems may periodically interrupt heating and/or coolingprovided by the VRF system and may lower overall efficiency of the VRFsystem. In a refrigeration cycle, oil drift and other associated issuesmay result in a specific indoor heat exchanger or an outdoor heatexchanger being utilized for an extended period of time which is oftendetrimental to overall operating conditions of the VRF systems. Further,if VRF devices (e.g., compressors) fail to receive enough oil, the VRFdevices may be at a higher risk of failure. While the traditional oilreturn systems can mitigate failure and other catastrophic issuesassociated with inaccurate oil return, there is often a significantimpact on efficiency of the VRF systems. For example, in periodic oilreturn, after transitioning to the oil return state, a compressor of theVRF system may need to decrease an operating speed and/or restart,thereby resulting in efficiency loss for the heating/cooling system.

As described in detail below, problems associated with traditional oilreturn systems (e.g., time-based oil return) can be addressed throughutilization of AI. AI can be used to predict an oil level in variousbuilding devices and viscosity of the oil. Based on said predictions,the AI can determine an optimal time to perform oil return when a VRFsystem is in cooling/heating operation. Advantageously, the predictionsperformed by the AI can be made without the use of an oil sensor. Nothaving to utilize oil sensors to detect oil states (e.g., oil level, oilviscosity, etc.) can reduce costs as fewer components need to bepurchased and maintained.

In some embodiments, AI models can be leveraged to manage vibrationsassociated with compressors of a VRF system. Excessive vibrations of thecompressors can lead to rapid degradation and thereby increased costsover a time period due to higher operational costs, maintenance costs,and replacement costs. To manage the vibrations, a particular AI model(e.g., an RNN model) can be trained to predict target currentsassociated with a direct axis (D-axis) and a quadrature axis (Q-axis).Prediction of the target currents can be based on inputs associated withthe compressors such as, for example, a frequency provided by aninverter, a real noise level, a q-axis feedback current, an axial errorbetween axes, etc. Using the predicted D-axis current and Q-axiscurrent, a correlated vibration of the compressors can be determined. Ifpredicted currents and/or predicted vibrations are too high (e.g., thepredicted currents are higher than thresholds on the current values),operation of the compressors can be modified to avoid excessivevibrations and thereby avoid rapid degradation of the compressors.Specifically, a constant input current and speed control can beimplemented to reduce vibrations.

In some embodiments, AI models can be leveraged to predict faultconditions of compressors in a VRF system. Fault conditions can refer tooperating states of the compressors that are beyond preferred operatingconditions. For example, fault conditions in a VRF system can includerefrigerant leakage, frosting of an outdoor unit, clogging of an indoorfan, a dirty indoor filter, a dirty heat exchanger, a dirty outdoor fan,motor demagnetization, compressor oil leakage, and other conditions thatresult in imperfect efficiency of compressors. To predict conditions, anAI model (e.g., an RNN model) can be trained to map values of inputsassociated with compressors to a fault classification. A faultclassification can include an indication of what fault conditions (ifany) are identified for a VRF system based on a set of input data. Forexample, the AI model may utilize inputs such as a compressor speed, anambient temperature, a discharge temperature, a suction pressure, adischarge pressure, an indoor fan mode, an outdoor fan step, etc. togenerate a fault classification. Based on the fault classification,various corrective actions can be initiated in response. As definedherein, a corrective action can refer to any action taken to address afault and/or some undesirable condition. For example, corrective actionsmay include scheduling maintenance, alerting a building operator to afault, disabling certain devices (e.g., certain compressors), operatingspecific devices, etc. In this way, faults affecting the compressors canbe addressed quickly and efficiently.

In some embodiments, AI models are utilized to predict an efficiency ofa motor in a VRF system (or other system). Motor efficiency can directlyimpact costs over a time period, in particular if a motor is operated ata high level of inefficiency. As efficiency of a motor decreases, costsmay increase due to additional resources (e.g., electricity, water,etc.) being consumed to generate a desired output. Moreover, the motormay degrade at a quicker rate as a result of needing to operate at amore intensive operating state (e.g., at a higher rotation per minute)to generate a desired output. In some embodiments, a current efficiencyof a motor can be represented as a percentage of how efficient the motoris in comparison to a maximum efficiency value when the motor wasoriginally installed. The motor efficiency can be determined based on anamount of resources required as input to result in a specific change.For example, efficiency of an electric motor in a compressor may bedefined by an amount of electricity required to compress a predefinedamount of a gas (e.g., air). In the example, a 50% efficiency of theelectric motor may indicate that the motor is half as efficient as itwas when originally installed and thereby requires twice as muchelectricity to compress a predefined amount of a gas. To predict anefficiency of a motor, an AI model can be trained to map a variety ofinputs (e.g., an amount of power consumed by the motor, an ambienttemperature, a rotations per minute (RPM) value of the motor, etc.) toan efficiency value of the motor.

It should be noted that while AI models used to predict vibrations,fault conditions, and motor efficiency are described separately, thepresent disclosure also contemplates one or more aggregate AI modelsthat predicts one or more of the aforementioned prediction targets. Forexample, a single AI model may be trained to predict currents associatedwith vibrations and fault conditions that may be affecting compressors.These and other features of the present disclosure are discussed indetail below.

Building HVAC Systems and Building Management Systems

Referring now to FIGS. 1-5 , several building management systems (BMS)and HVAC systems in which the systems and methods of the presentdisclosure can be implemented are shown, according to some embodiments.In brief overview, FIG. 1 shows a building 10 equipped with a HVACsystem 100. FIG. 2 is a block diagram of a waterside system 200 whichcan be used to serve building 10. FIG. 3 is a block diagram of anairside system 300 which can be used to serve building 10. FIG. 4 is ablock diagram of a BMS which can be used to monitor and control building10. FIG. 5 is a block diagram of another BMS which can be used tomonitor and control building 10.

Building and HVAC System

Referring particularly to FIG. 1 , a perspective view of a building 10is shown. Building 10 is served by a BMS. A BMS is, in general, a systemof devices configured to control, monitor, and manage equipment in oraround a building or building area. A BMS can include, for example, aHVAC system, a security system, a lighting system, a fire alertingsystem, any other system that is capable of managing building functionsor devices, or any combination thereof.

The BMS that serves building 10 includes a HVAC system 100. HVAC system100 can include a plurality of HVAC devices (e.g., heaters, chillers,air handling units, pumps, fans, thermal energy storage, etc.)configured to provide heating, cooling, ventilation, or other servicesfor building 10. For example, HVAC system 100 is shown to include awaterside system 120 and an airside system 130. Waterside system 120 mayprovide a heated or chilled fluid to an air handling unit of airsidesystem 130. Airside system 130 may use the heated or chilled fluid toheat or cool an airflow provided to building 10. An exemplary watersidesystem and airside system which can be used in HVAC system 100 aredescribed in greater detail with reference to FIGS. 2-3 .

HVAC system 100 is shown to include a chiller 102, a boiler 104, and arooftop air handling unit (AHU) 106. Waterside system 120 may use boiler104 and chiller 102 to heat or cool a working fluid (e.g., water,glycol, etc.) and may circulate the working fluid to AHU 106. In variousembodiments, the HVAC devices of waterside system 120 can be located inor around building 10 (as shown in FIG. 1 ) or at an offsite locationsuch as a central plant (e.g., a chiller plant, a steam plant, a heatplant, etc.). The working fluid can be heated in boiler 104 or cooled inchiller 102, depending on whether heating or cooling is required inbuilding 10. Boiler 104 may add heat to the circulated fluid, forexample, by burning a combustible material (e.g., natural gas) or usingan electric heating element. Chiller 102 may place the circulated fluidin a heat exchange relationship with another fluid (e.g., a refrigerant)in a heat exchanger (e.g., an evaporator) to absorb heat from thecirculated fluid. The working fluid from chiller 102 and/or boiler 104can be transported to AHU 106 via piping 108.

AHU 106 may place the working fluid in a heat exchange relationship withan airflow passing through AHU 106 (e.g., via one or more stages ofcooling coils and/or heating coils). The airflow can be, for example,outside air, return air from within building 10, or a combination ofboth. AHU 106 may transfer heat between the airflow and the workingfluid to provide heating or cooling for the airflow. For example, AHU106 can include one or more fans or blowers configured to pass theairflow over or through a heat exchanger containing the working fluid.The working fluid may then return to chiller 102 or boiler 104 viapiping 110.

Airside system 130 may deliver the airflow supplied by AHU 106 (i.e.,the supply airflow) to building 10 via air supply ducts 112 and mayprovide return air from building 10 to AHU 106 via air return ducts 114.In some embodiments, airside system 130 includes multiple variable airvolume (VAV) units 116. For example, airside system 130 is shown toinclude a separate VAV unit 116 on each floor or zone of building 10.VAV units 116 can include dampers or other flow control elements thatcan be operated to control an amount of the supply airflow provided toindividual zones of building 10. In other embodiments, airside system130 delivers the supply airflow into one or more zones of building 10(e.g., via supply ducts 112) without using intermediate VAV units 116 orother flow control elements. AHU 106 can include various sensors (e.g.,temperature sensors, pressure sensors, etc.) configured to measureattributes of the supply airflow. AHU 106 may receive input from sensorslocated within AHU 106 and/or within the building zone and may adjustthe flow rate, temperature, or other attributes of the supply airflowthrough AHU 106 to achieve setpoint conditions for the building zone.

Waterside System

Referring now to FIG. 2 , a block diagram of a waterside system 200 isshown, according to some embodiments. In various embodiments, watersidesystem 200 may supplement or replace waterside system 120 in HVAC system100 or can be implemented separate from HVAC system 100. Whenimplemented in HVAC system 100, waterside system 200 can include asubset of the HVAC devices in HVAC system 100 (e.g., boiler 104, chiller102, pumps, valves, etc.) and may operate to supply a heated or chilledfluid to AHU 106. The HVAC devices of waterside system 200 can belocated within building 10 (e.g., as components of waterside system 120)or at an offsite location such as a central plant.

In FIG. 2 , waterside system 200 is shown as a central plant having aplurality of subplants 202-212. Subplants 202-212 are shown to include aheater subplant 202, a heat recovery chiller subplant 204, a chillersubplant 206, a cooling tower subplant 208, a hot thermal energy storage(TES) subplant 210, and a cold thermal energy storage (TES) subplant212. Subplants 202-212 consume resources (e.g., water, natural gas,electricity, etc.) from utilities to serve thermal energy loads (e.g.,hot water, cold water, heating, cooling, etc.) of a building or campus.For example, heater subplant 202 can be configured to heat water in ahot water loop 214 that circulates the hot water between heater subplant202 and building 10. Chiller subplant 206 can be configured to chillwater in a cold water loop 216 that circulates the cold water betweenchiller subplant 206 building 10. Heat recovery chiller subplant 204 canbe configured to transfer heat from cold water loop 216 to hot waterloop 214 to provide additional heating for the hot water and additionalcooling for the cold water. Condenser water loop 218 may absorb heatfrom the cold water in chiller subplant 206 and reject the absorbed heatin cooling tower subplant 208 or transfer the absorbed heat to hot waterloop 214. Hot TES subplant 210 and cold TES subplant 212 may store hotand cold thermal energy, respectively, for subsequent use.

Hot water loop 214 and cold water loop 216 may deliver the heated and/orchilled water to air handlers located on the rooftop of building 10(e.g., AHU 106) or to individual floors or zones of building 10 (e.g.,VAV units 116). The air handlers push air past heat exchangers (e.g.,heating coils or cooling coils) through which the water flows to provideheating or cooling for the air. The heated or cooled air can bedelivered to individual zones of building 10 to serve thermal energyloads of building 10. The water then returns to subplants 202-212 toreceive further heating or cooling.

Although subplants 202-212 are shown and described as heating andcooling water for circulation to a building, it is understood that anyother type of working fluid (e.g., glycol, CO2, etc.) can be used inplace of or in addition to water to serve thermal energy loads. In otherembodiments, subplants 202-212 may provide heating and/or coolingdirectly to the building or campus without requiring an intermediateheat transfer fluid. These and other variations to waterside system 200are within the teachings of the present disclosure.

Each of subplants 202-212 can include a variety of equipment configuredto facilitate the functions of the subplant. For example, heatersubplant 202 is shown to include a plurality of heating elements 220(e.g., boilers, electric heaters, etc.) configured to add heat to thehot water in hot water loop 214. Heater subplant 202 is also shown toinclude several pumps 222 and 224 configured to circulate the hot waterin hot water loop 214 and to control the flow rate of the hot waterthrough individual heating elements 220. Chiller subplant 206 is shownto include a plurality of chillers 232 configured to remove heat fromthe cold water in cold water loop 216. Chiller subplant 206 is alsoshown to include several pumps 234 and 236 configured to circulate thecold water in cold water loop 216 and to control the flow rate of thecold water through individual chillers 232.

Heat recovery chiller subplant 204 is shown to include a plurality ofheat recovery heat exchangers 226 (e.g., refrigeration circuits)configured to transfer heat from cold water loop 216 to hot water loop214. Heat recovery chiller subplant 204 is also shown to include severalpumps 228 and 230 configured to circulate the hot water and/or coldwater through heat recovery heat exchangers 226 and to control the flowrate of the water through individual heat recovery heat exchangers 226.Cooling tower subplant 208 is shown to include a plurality of coolingtowers 238 configured to remove heat from the condenser water incondenser water loop 218. Cooling tower subplant 208 is also shown toinclude several pumps 240 configured to circulate the condenser water incondenser water loop 218 and to control the flow rate of the condenserwater through individual cooling towers 238.

Hot TES subplant 210 is shown to include a hot TES tank 242 configuredto store the hot water for later use. Hot TES subplant 210 may alsoinclude one or more pumps or valves configured to control the flow rateof the hot water into or out of hot TES tank 242. Cold TES subplant 212is shown to include cold TES tanks 244 configured to store the coldwater for later use. Cold TES subplant 212 may also include one or morepumps or valves configured to control the flow rate of the cold waterinto or out of cold TES tanks 244.

In some embodiments, one or more of the pumps in waterside system 200(e.g., pumps 222, 224, 228, 230, 234, 236, and/or 240) or pipelines inwaterside system 200 include an isolation valve associated therewith.Isolation valves can be integrated with the pumps or positioned upstreamor downstream of the pumps to control the fluid flows in watersidesystem 200. In various embodiments, waterside system 200 can includemore, fewer, or different types of devices and/or subplants based on theparticular configuration of waterside system 200 and the types of loadsserved by waterside system 200.

Airside System

Referring now to FIG. 3 , a block diagram of an airside system 300 isshown, according to some embodiments. In various embodiments, airsidesystem 300 may supplement or replace airside system 130 in HVAC system100 or can be implemented separate from HVAC system 100. Whenimplemented in HVAC system 100, airside system 300 can include a subsetof the HVAC devices in HVAC system 100 (e.g., AHU 106, VAV units 116,ducts 112-114, fans, dampers, etc.) and can be located in or aroundbuilding 10. Airside system 300 may operate to heat or cool an airflowprovided to building 10 using a heated or chilled fluid provided bywaterside system 200.

In FIG. 3 , airside system 300 is shown to include an economizer-typeair handling unit (AHU) 302. Economizer-type AHUs vary the amount ofoutside air and return air used by the air handling unit for heating orcooling. For example, AHU 302 may receive return air 304 from buildingzone 306 via return air duct 308 and may deliver supply air 310 tobuilding zone 306 via supply air duct 312. In some embodiments, AHU 302is a rooftop unit located on the roof of building 10 (e.g., AHU 106 asshown in FIG. 1 ) or otherwise positioned to receive both return air 304and outside air 314. AHU 302 can be configured to operate exhaust airdamper 316, mixing damper 318, and outside air damper 320 to control anamount of outside air 314 and return air 304 that combine to form supplyair 310. Any return air 304 that does not pass through mixing damper 318can be exhausted from AHU 302 through exhaust damper 316 as exhaust air322.

Each of dampers 316-320 can be operated by an actuator. For example,exhaust air damper 316 can be operated by actuator 324, mixing damper318 can be operated by actuator 326, and outside air damper 320 can beoperated by actuator 328. Actuators 324-328 may communicate with an AHUcontroller 330 via a communications link 332. Actuators 324-328 mayreceive control signals from AHU controller 330 and may provide feedbacksignals to AHU controller 330. Feedback signals can include, forexample, an indication of a current actuator or damper position, anamount of torque or force exerted by the actuator, diagnosticinformation (e.g., results of diagnostic tests performed by actuators324-328), status information, commissioning information, configurationsettings, calibration data, and/or other types of information or datathat can be collected, stored, or used by actuators 324-328. AHUcontroller 330 can be an economizer controller configured to use one ormore control algorithms (e.g., state-based algorithms, extremum seekingcontrol (ESC) algorithms, proportional-integral (PI) control algorithms,proportional-integral-derivative (PID) control algorithms, modelpredictive control (MPC) algorithms, feedback control algorithms, etc.)to control actuators 324-328.

Still referring to FIG. 3 , AHU 302 is shown to include a cooling coil334, a heating coil 336, and a fan 338 positioned within supply air duct312. Fan 338 can be configured to force supply air 310 through coolingcoil 334 and/or heating coil 336 and provide supply air 310 to buildingzone 306. AHU controller 330 may communicate with fan 338 viacommunications link 340 to control a flow rate of supply air 310. Insome embodiments, AHU controller 330 controls an amount of heating orcooling applied to supply air 310 by modulating a speed of fan 338.

Cooling coil 334 may receive a chilled fluid from waterside system 200(e.g., from cold water loop 216) via piping 342 and may return thechilled fluid to waterside system 200 via piping 344. Valve 346 can bepositioned along piping 342 or piping 344 to control a flow rate of thechilled fluid through cooling coil 334. In some embodiments, coolingcoil 334 includes multiple stages of cooling coils that can beindependently activated and deactivated (e.g., by AHU controller 330, byBMS controller 366, etc.) to modulate an amount of cooling applied tosupply air 310.

Heating coil 336 may receive a heated fluid from waterside system200(e.g., from hot water loop 214) via piping 348 and may return theheated fluid to waterside system 200 via piping 350. Valve 352 can bepositioned along piping 348 or piping 350 to control a flow rate of theheated fluid through heating coil 336. In some embodiments, heating coil336 includes multiple stages of heating coils that can be independentlyactivated and deactivated (e.g., by AHU controller 330, by BMScontroller 366, etc.) to modulate an amount of heating applied to supplyair 310.

Each of valves 346 and 352 can be controlled by an actuator. Forexample, valve 346 can be controlled by actuator 354 and valve 352 canbe controlled by actuator 356. Actuators 354-356 may communicate withAHU controller 330 via communications links 358-360. Actuators 354-356may receive control signals from AHU controller 330 and may providefeedback signals to controller 330. In some embodiments, AHU controller330 receives a measurement of the supply air temperature from atemperature sensor 362 positioned in supply air duct 312 (e.g.,downstream of cooling coil 334 and/or heating coil 336). AHU controller330 may also receive a measurement of the temperature of building zone306 from a temperature sensor 364 located in building zone 306.

In some embodiments, AHU controller 330 operates valves 346 and 352 viaactuators 354-356 to modulate an amount of heating or cooling providedto supply air 310 (e.g., to achieve a setpoint temperature for supplyair 310 or to maintain the temperature of supply air 310 within asetpoint temperature range). The positions of valves 346 and 352 affectthe amount of heating or cooling provided to supply air 310 by coolingcoil 334 or heating coil 336 and may correlate with the amount of energyconsumed to achieve a desired supply air temperature. AHU 330 maycontrol the temperature of supply air 310 and/or building zone 306 byactivating or deactivating coils 334-336, adjusting a speed of fan 338,or a combination of both.

Still referring to FIG. 3 , airside system 300 is shown to include abuilding management system (BMS) controller 366 and a client device 368.BMS controller 366 can include one or more computer systems (e.g.,servers, supervisory controllers, subsystem controllers, etc.) thatserve as system level controllers, application or data servers, headnodes, or master controllers for airside system 300, waterside system200, HVAC system 100, and/or other controllable systems that servebuilding 10. BMS controller 366 may communicate with multiple downstreambuilding systems or subsystems (e.g., HVAC system 100, a securitysystem, a lighting system, waterside system 200, etc.) via acommunications link 370 according to like or disparate protocols (e.g.,LON, BACnet, etc.). In various embodiments, AHU controller 330 and BMScontroller 366 can be separate (as shown in FIG. 3 ) or integrated. Inan integrated implementation, AHU controller 330 can be a softwaremodule configured for execution by a processor of BMS controller 366.

In some embodiments, AHU controller 330 receives information from BMScontroller 366 (e.g., commands, setpoints, operating boundaries, etc.)and provides information to BMS controller 366 (e.g., temperaturemeasurements, valve or actuator positions, operating statuses,diagnostics, etc.). For example, AHU controller 330 may provide BMScontroller 366 with temperature measurements from temperature sensors362-364, equipment on/off states, equipment operating capacities, and/orany other information that can be used by BMS controller 366 to monitoror control a variable state or condition within building zone 306.

Client device 368 can include one or more human-machine interfaces orclient interfaces (e.g., graphical user interfaces, reportinginterfaces, text-based computer interfaces, client-facing web services,web servers that provide pages to web clients, etc.) for controlling,viewing, or otherwise interacting with HVAC system 100, its subsystems,and/or devices. Client device 368 can be a computer workstation, aclient terminal, a remote or local interface, or any other type of userinterface device. Client device 368 can be a stationary terminal or amobile device. For example, client device 368 can be a desktop computer,a computer server with a user interface, a laptop computer, a tablet, asmartphone, a PDA, or any other type of mobile or non-mobile device.Client device 368 may communicate with BMS controller 366 and/or AHUcontroller 330 via communications link 372.

Building Management Systems

Referring now to FIG. 4 , a block diagram of a building managementsystem (BMS) 400 is shown, according to some embodiments. BMS 400 can beimplemented in building 10 to automatically monitor and control variousbuilding functions. BMS 400 is shown to include BMS controller 366 and aplurality of building subsystems 428. Building subsystems 428 are shownto include a building electrical subsystem 434, an informationcommunication technology (ICT) subsystem 436, a security subsystem 438,a HVAC subsystem 440, a lighting subsystem 442, a lift/escalatorssubsystem 432, and a fire safety subsystem 430. In various embodiments,building subsystems 428 can include fewer, additional, or alternativesubsystems. For example, building subsystems 428 may also oralternatively include a refrigeration subsystem, an advertising orsignage subsystem, a cooking subsystem, a vending subsystem, a printeror copy service subsystem, or any other type of building subsystem thatuses controllable equipment and/or sensors to monitor or controlbuilding 10. In some embodiments, building subsystems 428 includewaterside system 200 and/or airside system 300, as described withreference to FIGS. 2-3 .

Each of building subsystems 428 can include any number of devices,controllers, and connections for completing its individual functions andcontrol activities. HVAC subsystem 440 can include many of the samecomponents as HVAC system 100, as described with reference to FIGS. 1-3. For example, HVAC subsystem 440 can include a chiller, a boiler, anynumber of air handling units, economizers, field controllers,supervisory controllers, actuators, temperature sensors, and otherdevices for controlling the temperature, humidity, airflow, or othervariable conditions within building 10. Lighting subsystem 442 caninclude any number of light fixtures, ballasts, lighting sensors,dimmers, or other devices configured to controllably adjust the amountof light provided to a building space. Security subsystem 438 caninclude occupancy sensors, video surveillance cameras, digital videorecorders, video processing servers, intrusion detection devices, accesscontrol devices and servers, or other security-related devices.

Still referring to FIG. 4 , BMS controller 366 is shown to include acommunications interface 407 and a BMS interface 409. Interface 407 mayfacilitate communications between BMS controller 366 and externalapplications (e.g., monitoring and reporting applications 422,enterprise control applications 426, remote systems and applications444, applications residing on client devices 448, etc.) for allowinguser control, monitoring, and adjustment to BMS controller 366 and/orsubsystems 428. Interface 407 may also facilitate communications betweenBMS controller 366 and client devices 448. BMS interface 409 mayfacilitate communications between BMS controller 366 and buildingsubsystems 428 (e.g., HVAC, lighting security, lifts, powerdistribution, business, etc.).

Interfaces 407, 409 can be or include wired or wireless communicationsinterfaces (e.g., jacks, antennas, transmitters, receivers,transceivers, wire terminals, etc.) for conducting data communicationswith building subsystems 428 or other external systems or devices. Invarious embodiments, communications via interfaces 407, 409 can bedirect (e.g., local wired or wireless communications) or via acommunications network 446 (e.g., a WAN, the Internet, a cellularnetwork, etc.). For example, interfaces 407, 409 can include an Ethernetcard and port for sending and receiving data via an Ethernet-basedcommunications link or network. In another example, interfaces 407, 409can include a Wi-Fi transceiver for communicating via a wirelesscommunications network. In another example, one or both of interfaces407, 409 can include cellular or mobile phone communicationstransceivers. In one embodiment, communications interface 407 is a powerline communications interface and BMS interface 409 is an Ethernetinterface. In other embodiments, both communications interface 407 andBMS interface 409 are Ethernet interfaces or are the same Ethernetinterface.

Still referring to FIG. 4 , BMS controller 366 is shown to include aprocessing circuit 404 including a processor 406 and memory 408.Processing circuit 404 can be communicably connected to BMS interface409 and/or communications interface 407 such that processing circuit 404and the various components thereof can send and receive data viainterfaces 407, 409. Processor 406 can be implemented as a generalpurpose processor, an application specific integrated circuit (ASIC),one or more field programmable gate arrays (FPGAs), a group ofprocessing components, or other suitable electronic processingcomponents.

Memory 408 (e.g., memory, memory unit, storage device, etc.) can includeone or more devices (e.g., RAM, ROM, Flash memory, hard disk storage,etc.) for storing data and/or computer code for completing orfacilitating the various processes, layers and modules described in thepresent application. Memory 408 can be or include volatile memory ornon-volatile memory. Memory 408 can include database components, objectcode components, script components, or any other type of informationstructure for supporting the various activities and informationstructures described in the present application. According to someembodiments, memory 408 is communicably connected to processor 406 viaprocessing circuit 404 and includes computer code for executing (e.g.,by processing circuit 404 and/or processor 406) one or more processesdescribed herein.

In some embodiments, BMS controller 366 is implemented within a singlecomputer (e.g., one server, one housing, etc.). In various otherembodiments BMS controller 366 can be distributed across multipleservers or computers (e.g., that can exist in distributed locations).Further, while FIG. 4 shows applications 422 and 426 as existing outsideof BMS controller 366, in some embodiments, applications 422 and 426 canbe hosted within BMS controller 366 (e.g., within memory 408).

Still referring to FIG. 4 , memory 408 is shown to include an enterpriseintegration layer 410, an automated measurement and validation (AM&V)layer 412, a demand response (DR) layer 414, a fault detection anddiagnostics (FDD) layer 416, an integrated control layer 418, and abuilding subsystem integration later 420. Layers 410-420 can beconfigured to receive inputs from building subsystems 428 and other datasources, determine optimal control actions for building subsystems 428based on the inputs, generate control signals based on the optimalcontrol actions, and provide the generated control signals to buildingsubsystems 428. The following paragraphs describe some of the generalfunctions performed by each of layers 410-420 in BMS 400.

Enterprise integration layer 410 can be configured to serve clients orlocal applications with information and services to support a variety ofenterprise-level applications. For example, enterprise controlapplications 426 can be configured to provide subsystem-spanning controlto a graphical user interface (GUI) or to any number of enterprise-levelbusiness applications (e.g., accounting systems, user identificationsystems, etc.). Enterprise control applications 426 may also oralternatively be configured to provide configuration GUIs forconfiguring BMS controller 366. In yet other embodiments, enterprisecontrol applications 426 can work with layers 410-420 to optimizebuilding performance (e.g., efficiency, energy use, comfort, or safety)based on inputs received at interface 407 and/or BMS interface 409.

Building subsystem integration layer 420 can be configured to managecommunications between BMS controller 366 and building subsystems 428.For example, building subsystem integration layer 420 may receive sensordata and input signals from building subsystems 428 and provide outputdata and control signals to building subsystems 428. Building subsystemintegration layer 420 may also be configured to manage communicationsbetween building subsystems 428. Building subsystem integration layer420 translate communications (e.g., sensor data, input signals, outputsignals, etc.) across a plurality of multi-vendor/multi-protocolsystems.

Demand response layer 414 can be configured to optimize resource usage(e.g., electricity use, natural gas use, water use, etc.) and/or themonetary cost of such resource usage in response to satisfy the demandof building 10. The optimization can be based on time-of-use prices,curtailment signals, energy availability, or other data received fromutility providers, distributed energy generation systems 424, fromenergy storage 427 (e.g., hot TES 242, cold TES 244, etc.), or fromother sources. Demand response layer 414 may receive inputs from otherlayers of BMS controller 366 (e.g., building subsystem integration layer420, integrated control layer 418, etc.). The inputs received from otherlayers can include environmental or sensor inputs such as temperature,carbon dioxide levels, relative humidity levels, air quality sensoroutputs, occupancy sensor outputs, room schedules, and the like. Theinputs may also include inputs such as electrical use (e.g., expressedin kWh), thermal load measurements, pricing information, projectedpricing, smoothed pricing, curtailment signals from utilities, and thelike.

According to some embodiments, demand response layer 414 includescontrol logic for responding to the data and signals it receives. Theseresponses can include communicating with the control algorithms inintegrated control layer 418, changing control strategies, changingsetpoints, or activating/deactivating building equipment or subsystemsin a controlled manner. Demand response layer 414 may also includecontrol logic configured to determine when to utilize stored energy. Forexample, demand response layer 414 may determine to begin using energyfrom energy storage 427 just prior to the beginning of a peak use hour.

In some embodiments, demand response layer 414 includes a control moduleconfigured to actively initiate control actions (e.g., automaticallychanging setpoints) which minimize energy costs based on one or moreinputs representative of or based on demand (e.g., price, a curtailmentsignal, a demand level, etc.). In some embodiments, demand responselayer 414 uses equipment models to determine an optimal set of controlactions. The equipment models can include, for example, thermodynamicmodels describing the inputs, outputs, and/or functions performed byvarious sets of building equipment. Equipment models may representcollections of building equipment (e.g., subplants, chiller arrays,etc.) or individual devices (e.g., individual chillers, heaters, pumps,etc.).

Demand response layer 414 may further include or draw upon one or moredemand response policy definitions (e.g., databases, XML files, etc.).The policy definitions can be edited or adjusted by a user (e.g., via agraphical user interface) so that the control actions initiated inresponse to demand inputs can be tailored for the user's application,desired comfort level, particular building equipment, or based on otherconcerns. For example, the demand response policy definitions canspecify which equipment can be turned on or off in response toparticular demand inputs, how long a system or piece of equipment shouldbe turned off, what setpoints can be changed, what the allowable setpoint adjustment range is, how long to hold a high demand setpointbefore returning to a normally scheduled setpoint, how close to approachcapacity limits, which equipment modes to utilize, the energy transferrates (e.g., the maximum rate, an alarm rate, other rate boundaryinformation, etc.) into and out of energy storage devices (e.g., thermalstorage tanks, battery banks, etc.), and when to dispatch on-sitegeneration of energy (e.g., via fuel cells, a motor generator set,etc.).

Integrated control layer 418 can be configured to use the data input oroutput of building subsystem integration layer 420 and/or demandresponse later 414 to make control decisions. Due to the subsystemintegration provided by building subsystem integration layer 420,integrated control layer 418 can integrate control activities of thesubsystems 428 such that the subsystems 428 behave as a singleintegrated supersystem. In some embodiments, integrated control layer418 includes control logic that uses inputs and outputs from a pluralityof building subsystems to provide greater comfort and energy savingsrelative to the comfort and energy savings that separate subsystemscould provide alone. For example, integrated control layer 418 can beconfigured to use an input from a first subsystem to make anenergy-saving control decision for a second subsystem. Results of thesedecisions can be communicated back to building subsystem integrationlayer 420.

Integrated control layer 418 is shown to be logically below demandresponse layer 414. Integrated control layer 418 can be configured toenhance the effectiveness of demand response layer 414 by enablingbuilding subsystems 428 and their respective control loops to becontrolled in coordination with demand response layer 414. Thisconfiguration may advantageously reduce disruptive demand responsebehavior relative to conventional systems. For example, integratedcontrol layer 418 can be configured to assure that a demandresponse-driven upward adjustment to the setpoint for chilled watertemperature (or another component that directly or indirectly affectstemperature) does not result in an increase in fan energy (or otherenergy used to cool a space) that would result in greater total buildingenergy use than was saved at the chiller.

Integrated control layer 418 can be configured to provide feedback todemand response layer 414 so that demand response layer 414 checks thatconstraints (e.g., temperature, lighting levels, etc.) are properlymaintained even while demanded load shedding is in progress. Theconstraints may also include setpoint or sensed boundaries relating tosafety, equipment operating limits and performance, comfort, fire codes,electrical codes, energy codes, and the like. Integrated control layer418 is also logically below fault detection and diagnostics layer 416and automated measurement and validation layer 412. Integrated controllayer 418 can be configured to provide calculated inputs (e.g.,aggregations) to these higher levels based on outputs from more than onebuilding subsystem.

Automated measurement and validation (AM&V) layer 412 can be configuredto verify that control strategies commanded by integrated control layer418 or demand response layer 414 are working properly (e.g., using dataaggregated by AM&V layer 412, integrated control layer 418, buildingsubsystem integration layer 420, FDD layer 416, or otherwise). Thecalculations made by AM&V layer 412 can be based on building systemenergy models and/or equipment models for individual BMS devices orsubsystems. For example, AM&V layer 412 may compare a model-predictedoutput with an actual output from building subsystems 428 to determinean accuracy of the model.

Fault detection and diagnostics (FDD) layer 416 can be configured toprovide on-going fault detection for building subsystems 428, buildingsubsystem devices (i.e., building equipment), and control algorithmsused by demand response layer 414 and integrated control layer 418. FDDlayer 416 may receive data inputs from integrated control layer 418,directly from one or more building subsystems or devices, or fromanother data source. FDD layer 416 may automatically diagnose andrespond to detected faults. The responses to detected or diagnosedfaults can include providing an alert message to a user, a maintenancescheduling system, or a control algorithm configured to attempt torepair the fault or to work-around the fault.

FDD layer 416 can be configured to output a specific identification ofthe faulty component or cause of the fault (e.g., loose damper linkage)using detailed subsystem inputs available at building subsystemintegration layer 420. In other exemplary embodiments, FDD layer 416 isconfigured to provide “fault” events to integrated control layer 418which executes control strategies and policies in response to thereceived fault events. According to some embodiments, FDD layer 416 (ora policy executed by an integrated control engine or business rulesengine) may shut-down systems or direct control activities around faultydevices or systems to reduce energy waste, extend equipment life, orassure proper control response.

FDD layer 416 can be configured to store or access a variety ofdifferent system data stores (or data points for live data). FDD layer416 may use some content of the data stores to identify faults at theequipment level (e.g., specific chiller, specific AHU, specific terminalunit, etc.) and other content to identify faults at component orsubsystem levels. For example, building subsystems 428 may generatetemporal (i.e., time-series) data indicating the performance of BMS 400and the various components thereof. The data generated by buildingsubsystems 428 can include measured or calculated values that exhibitstatistical characteristics and provide information about how thecorresponding system or process (e.g., a temperature control process, aflow control process, etc.) is performing in terms of error from itssetpoint. These processes can be examined by FDD layer 416 to exposewhen the system begins to degrade in performance and alert a user torepair the fault before it becomes more severe.

Referring now to FIG. 5 , a block diagram of another building managementsystem (BMS) 500 is shown, according to some embodiments. BMS 500 can beused to monitor and control the devices of HVAC system 100, watersidesystem 200, airside system 300, building subsystems 428, as well asother types of BMS devices (e.g., lighting equipment, securityequipment, etc.) and/or HVAC equipment.

BMS 500 provides a system architecture that facilitates automaticequipment discovery and equipment model distribution. Equipmentdiscovery can occur on multiple levels of BMS 500 across multipledifferent communications busses (e.g., a system bus 554, zone buses556-560 and 564, sensor/actuator bus 566, etc.) and across multipledifferent communications protocols. In some embodiments, equipmentdiscovery is accomplished using active node tables, which provide statusinformation for devices connected to each communications bus. Forexample, each communications bus can be monitored for new devices bymonitoring the corresponding active node table for new nodes. When a newdevice is detected, BMS 500 can begin interacting with the new device(e.g., sending control signals, using data from the device) without userinteraction.

Some devices in BMS 500 present themselves to the network usingequipment models. An equipment model defines equipment objectattributes, view definitions, schedules, trends, and the associatedBACnet value objects (e.g., analog value, binary value, multistatevalue, etc.) that are used for integration with other systems. Somedevices in BMS 500 store their own equipment models. Other devices inBMS 500 have equipment models stored externally (e.g., within otherdevices). For example, a zone coordinator 508 can store the equipmentmodel for a bypass damper 528. In some embodiments, zone coordinator 508automatically creates the equipment model for bypass damper 528 or otherdevices on zone bus 558. Other zone coordinators can also createequipment models for devices connected to their zone busses. Theequipment model for a device can be created automatically based on thetypes of data points exposed by the device on the zone bus, device type,and/or other device attributes. Several examples of automatic equipmentdiscovery and equipment model distribution are discussed in greaterdetail below.

Still referring to FIG. 5 , BMS 500 is shown to include a system manager502; several zone coordinators 506, 508, 510 and 518; and several zonecontrollers 524, 530, 532, 536, 548, and 550. System manager 502 canmonitor data points in BMS 500 and report monitored variables to variousmonitoring and/or control applications. System manager 502 cancommunicate with client devices 504 (e.g., user devices, desktopcomputers, laptop computers, mobile devices, etc.) via a datacommunications link 574 (e.g., BACnet IP, Ethernet, wired or wirelesscommunications, etc.). System manager 502 can provide a user interfaceto client devices 504 via data communications link 574. The userinterface may allow users to monitor and/or control BMS 500 via clientdevices 504.

In some embodiments, system manager 502 is connected with zonecoordinators 506-510 and 518 via a system bus 554. System manager 502can be configured to communicate with zone coordinators 506-510 and 518via system bus 554 using a master-slave token passing (MSTP) protocol orany other communications protocol. System bus 554 can also connectsystem manager 502 with other devices such as a constant volume (CV)rooftop unit (RTU) 512, an input/output module (TOM) 514, a thermostatcontroller 516 (e.g., a TEC5000 series thermostat controller), and anetwork automation engine (NAE) or third-party controller 520. RTU 512can be configured to communicate directly with system manager 502 andcan be connected directly to system bus 554. Other RTUs can communicatewith system manager 502 via an intermediate device. For example, a wiredinput 562 can connect a third-party RTU 542 to thermostat controller516, which connects to system bus 554.

System manager 502 can provide a user interface for any devicecontaining an equipment model. Devices such as zone coordinators 506-510and 518 and thermostat controller 516 can provide their equipment modelsto system manager 502 via system bus 554. In some embodiments, systemmanager 502 automatically creates equipment models for connected devicesthat do not contain an equipment model (e.g., IOM 514, third partycontroller 520, etc.). For example, system manager 502 can create anequipment model for any device that responds to a device tree request.The equipment models created by system manager 502 can be stored withinsystem manager 502. System manager 502 can then provide a user interfacefor devices that do not contain their own equipment models using theequipment models created by system manager 502. In some embodiments,system manager 502 stores a view definition for each type of equipmentconnected via system bus 554 and uses the stored view definition togenerate a user interface for the equipment.

Each zone coordinator 506-510 and 518 can be connected with one or moreof zone controllers 524, 530-532, 536, and 548-550 via zone buses 556,558, 560, and 564. Zone coordinators 506-510 and 518 can communicatewith zone controllers 524, 530-532, 536, and 548-550 via zone busses556-560 and 564 using a MSTP protocol or any other communicationsprotocol. Zone busses 556-560 and 564 can also connect zone coordinators506-510 and 518 with other types of devices such as variable air volume(VAV) RTUs 522 and 540, changeover bypass (COBP) RTUs 526 and 552,bypass dampers 528 and 546, and PEAK controllers 534 and 544.

Zone coordinators 506-510 and 518 can be configured to monitor andcommand various zoning systems. In some embodiments, each zonecoordinator 506-510 and 518 monitors and commands a separate zoningsystem and is connected to the zoning system via a separate zone bus.For example, zone coordinator 506 can be connected to VAV RTU 522 andzone controller 524 via zone bus 556. Zone coordinator 508 can beconnected to COBP RTU 526, bypass damper 528, COBP zone controller 530,and VAV zone controller 532 via zone bus 558. Zone coordinator 510 canbe connected to PEAK controller 534 and VAV zone controller 536 via zonebus 560. Zone coordinator 518 can be connected to PEAK controller 544,bypass damper 546, COBP zone controller 548, and VAV zone controller 550via zone bus 564.

A single model of zone coordinator 506-510 and 518 can be configured tohandle multiple different types of zoning systems (e.g., a VAV zoningsystem, a COBP zoning system, etc.). Each zoning system can include aRTU, one or more zone controllers, and/or a bypass damper. For example,zone coordinators 506 and 510 are shown as Verasys VAV engines (VVEs)connected to VAV RTUs 522 and 540, respectively. Zone coordinator 506 isconnected directly to VAV RTU 522 via zone bus 556, whereas zonecoordinator 510 is connected to a third-party VAV RTU 540 via a wiredinput 568 provided to PEAK controller 534. Zone coordinators 508 and 518are shown as Verasys COBP engines (VCEs) connected to COBP RTUs 526 and552, respectively. Zone coordinator 508 is connected directly to COBPRTU 526 via zone bus 558, whereas zone coordinator 518 is connected to athird-party COBP RTU 552 via a wired input 570 provided to PEAKcontroller 544.

Zone controllers 524, 530-532, 536, and 548-550 can communicate withindividual BMS devices (e.g., sensors, actuators, etc.) viasensor/actuator (SA) busses. For example, VAV zone controller 536 isshown connected to networked sensors 538 via SA bus 566. Zone controller536 can communicate with networked sensors 538 using a MSTP protocol orany other communications protocol. Although only one SA bus 566 is shownin FIG. 5 , it should be understood that each zone controller 524,530-532, 536, and 548-550 can be connected to a different SA bus. EachSA bus can connect a zone controller with various sensors (e.g.,temperature sensors, humidity sensors, pressure sensors, light sensors,occupancy sensors, etc.), actuators (e.g., damper actuators, valveactuators, etc.) and/or other types of controllable equipment (e.g.,chillers, heaters, fans, pumps, etc.).

Each zone controller 524, 530-532, 536, and 548-550 can be configured tomonitor and control a different building zone. Zone controllers 524,530-532, 536, and 548-550 can use the inputs and outputs provided viatheir SA busses to monitor and control various building zones. Forexample, a zone controller 536 can use a temperature input received fromnetworked sensors 538 via SA bus 566 (e.g., a measured temperature of abuilding zone) as feedback in a temperature control algorithm. Zonecontrollers 524, 530-532, 536, and 548-550 can use various types ofcontrol algorithms (e.g., state-based algorithms, extremum seekingcontrol (ESC) algorithms, proportional-integral (PI) control algorithms,proportional-integral-derivative (PID) control algorithms, modelpredictive control (MPC) algorithms, feedback control algorithms, etc.)to control a variable state or condition (e.g., temperature, humidity,airflow, lighting, etc.) in or around building 10.

Variable Refrigerant Flow System

Referring now to FIGS. 6A-6B, a variable refrigerant flow (VRF) system600 is shown, according to some embodiments. VRF system 600 is shown toinclude multiple outdoor VRF units 602 and multiple indoor VRF units604. Outdoor VRF units 602 can be located outside a building and canoperate to heat or cool a refrigerant. Outdoor VRF units 602 can consumeelectricity to convert refrigerant between liquid, gas, and/orsuper-heated gas phases. Indoor VRF units 604 can be distributedthroughout various building zones within a building and can receive theheated or cooled refrigerant from outdoor VRF units 602. Each indoor VRFunit 604 can provide temperature control for the particular buildingzone in which the indoor VRF unit is located.

A primary advantage of VRF systems is that some indoor VRF units 604 canoperate in a cooling mode while other indoor VRF units 604 operate in aheating mode. For example, each of outdoor VRF units 602 and indoor VRFunits 604 can operate in a heating mode, a cooling mode, or an off mode.Each building zone can be controlled independently and can havedifferent temperature setpoints. In some embodiments, each building hasup to three outdoor VRF units 602 located outside the building (e.g., ona rooftop) and up to 128 indoor VRF units 604 distributed throughout thebuilding (e.g., in various building zones).

Many different configurations exist for VRF system 600. In someembodiments, VRF system 600 is a two-pipe system in which each outdoorVRF unit 602 connects to a single refrigerant return line and a singlerefrigerant outlet line. In a two-pipe system, all of the outdoor VRFunits 602 operate in the same mode since only one of a heated or chilledrefrigerant can be provided via the single refrigerant outlet line. Inother embodiments, VRF system 600 is a three-pipe system in which eachoutdoor VRF unit 602 connects to a refrigerant return line, a hotrefrigerant outlet line, and a cold refrigerant outlet line. In athree-pipe system, both heating and cooling can be providedsimultaneously via dual refrigerant outlet lines.

VRF system 600 can represent an example of a VRF system that may utilizeAI to predict oil level and viscosity to ensure components (e.g.,outdoor VRF units 602, indoor VRF units 604, etc.) are operatingcorrectly. Specifically, the components may require oil for adequatelubrication to ensure that rapid degradation of the components areavoided. Specifically, VRF system 600 may leverage the systems andmethods described throughout FIGS. 7-10 to ensure that all componentshave an adequate supply of oil, that the oil provided is of anappropriate viscosity, etc.

Referring now to FIG. 7A, an illustration of a VRF system 700 is shown,according to some embodiments. In some embodiments, VRF system 700 issimilar to and/or the same as VRF system 600 as described with referenceto FIGS. 6A and 6B. More particularly, VRF system 700 can illustrate howoil is utilized in a VRF system. It should be noted that VRF system 700is shown purely for sake of example of how a VRF system may operate.Components, relationships, and/or other features of VRF system 700 canbe customized and configured based on particular implementations. Forexample, VRF system 700 may include more or fewer compressors 701 thanas shown in FIG. 7A.

VRF system 700 is shown to include compressors 701, a heat exchanger702, a double tube type heat exchanger 703, oil separators 704, and anaccumulator 705. To operate properly, compressors 701 may require oil toensure components of compressors 701 are properly lubricated. Withoutoil, the components may rapidly degrade and compressors 701 may fail toprovide adequate cooling/heating to a zone. Heat exchangers 702 and 703can transfer heat between fluids (e.g., oil and a refrigerant). Oilseparators 704 can separate oil from refrigerant and/or other fluids inVRF system 700. Specifically, during operation, compressors 701 may leakand/or otherwise allow some oil to get mixed into refrigerant outputtedby compressors 701. If said oil is not extracted back out of theoil/refrigerant mixture, the oil may be provided to components beyondVRF system 700 (e.g., indoor AHUs) which can result in a rapid loss ofoil in VRF system 700. Accordingly, oil separators 704 can distill theoil from the fluid mixtures and provide the oil to accumulator 705 fortemporary storage. Accumulator 705 can store the oil and can be accessedas needed to retrieve oil for other components of VRF system 700.

VRF system 700 is also shown to include strainers 706, a distributor707, reversing valves 708, capillary tubes 709, and micro-computercontrol expansion valves 710. Strainers 706 can remove impurities (e.g.,dirt, debris, etc.) from the oil and/or the oil/refrigerant mixture thatmay be accidentally integrated with the oil and/or oil/refrigerantmixture during operation of VRF system 700. Impurities can result inpoor functioning of building equipment and may affect characteristics ofthe oil in VRF system 700 (e.g., by increasing or decreasing a viscosityof the oil). Distributor 707 can help in distributing fluids throughoutheat exchanger 702. Reversing valves 708 can change a direction ofrefrigerant flow in VRF system 700 to switch VRF system 700 betweenheating and cooling modes. Capillary tubes 709 can assist in reducing atemperature of refrigerant in VRF system 700 by affecting a pressure ofthe refrigerant. Micro-computer control expansion valves 710 canregulate an amount of refrigerant entering components of VRF system 700.

VRF system 700 is also shown to include check valves 711, solenoidvalves 712, check joints 713, a stop valve 714 for the liquid line, astop valve 715 for the gas (low) line, a stop valve 716 for the gas(high/low) line, a refrigerant pressure sensor 717, another refrigerantpressure sensor 718, and high pressure switches 719. Check valves 711can help ensure that fluid is flowing in a correct direction within VRFsystem 700 by restricting the fluid from flowing in a direction oppositea desired direction of flow. Solenoid valves 712 can regulate a flow offluids in VRF system 700. Check joints 713 can help regulate stress oncomponents of VRF system 700. Stop valves 714, 715, and 716 can restricta flow of fluid in the liquid line, the gas (low) line, and the gas(high/low) line shown in the illustration of FIG. 7A, respectively. Withregard to refrigerant pressure sensors 717 and 718, refrigerant pressuresensor 717 may be a high pressure sensor whereas refrigerant pressuresensor 718 may be a low pressure sensor within VRF system 700. Asrefrigerant returns to compressors 701, high pressure switches 719 canstop the refrigerant from entering compressors 701 if a pressure of therefrigerant is too high or too low in order to prevent damage tocompressors 701.

VRF system 700 is also shown to include a variety of thermistors. In VRFsystem 700, a resistance across a thermistor can be primarily based on atemperature of a connected component. In the illustration of FIG. 7A,VRF system 700 is shown to include thermistors 720-725. Thermistor 720is associated with an upper side of first compressor 701. Thermistor 721is associated with an upper side of second compressor 701. Thermistor722 is associated with a gas side of heat exchanger 702. Thermistor 723is associated with a liquid side of heat exchanger 702. Thermistor 724is associated with a subcooler bypass side. Thermistor 725 is associatedwith an auto charge of refrigerant.

Each pipe in VRF system 700 is also labeled with a corresponding outerdiameter OD and a thickness T which are given below in Table A. Asshould be noted, the material used in VRF system 700 across all pipingis C1220T-O.

TABLE A Outer Diameter and Thickness of Piping Mark OD × T Material a 13/32 × 0.075 C1220T-O [28.0] × [1.9] b 1 3/32 × 0.063 [28.0] × [1.6] c 1× 0.071 [25.4] × [1.8] d 1 × 0.047 [25.4] × [1.2] e ⅞ × 0.059 [22.0] ×[1.5] f ⅞ × 0.047 [22.0] × [1.2] g ¾ × 0.065 [19.05] × [1.65] h ⅝ ×0.047 [15.88] × [1.2] i ½ × 0.039 [12.7] × [1.0] j ⅜ × 0.031 [9.52] ×[0.8] k ¼ × 0.042 [6.35] × [1.07] l ¼ × 0.028 [6.35] × [0.7]

Referring now to FIG. 7B, an illustration of a VRF system 750 is shown,according to some embodiments. In some embodiments, VRF system 750 issimilar to and/or the same as VRF system 700 as described with referenceto FIG. 7 and/or VRF system 600 as described with reference to FIGS. 6Aand 6B. Specifically, VRF system 750 illustrates a flow of oilthroughout a VRF system. As with VRF system 700, VRF system 750 isprovided purely for sake of example. The components, structure, and/orother characteristics of VRF system 750 can be customized and configureddependent on implementation.

VRF system 750 is shown to include a suction line 752. Suction line 752can provide refrigerant (e.g., refrigerant vapor) used by one or moredevices/systems (e.g., an indoor AHU) to compressors 754. In someembodiments, some oil may be included in fluid provided to compressors754 by suction line 752. Based on the received refrigerant, compressors754 can operate to compress the refrigerant into a higher pressure gasand output said higher pressure gas via a discharge line 756. Ascompressors 754 may require oil to function properly, the compressionprocess performed by compressors 754 may result in some oil gettingmixed into the outputted high pressure gas, thereby resulting in anoil/refrigerant mixture.

VRF system 750 is also shown to include an oil separator 758. Based onthe received oil/refrigerant mixture, oil separator 758 can operate toseparate the oil from the refrigerant. After separation, the refrigerantcan be provided to some device/system (e.g., an indoor AHU) via arefrigerant line 762. The separated oil can be provided to anaccumulator 760 via an oil line 764. Accumulator 760 can function as astorage container for oil separated by oil separator 758. Accumulator760 may have some maximum capacity that defines a maximum amount of oilthat can be stored in accumulator 760.

Oil stored by accumulator 760 can be provided back to compressors 754via an oil return line 766. Given that accumulator 760 has some non-zeroamount of stored oil, accumulator 760 can provide the oil to any ofcompressors 754 if a particular compressor 754 requires more oil. Insome embodiments, VRF system 750 includes valves 768 that regulate aflow of oil to compressors 754. In this case, valves 768 may prevent toomuch oil from being provided to compressors 754 and/or otherwiseregulate oil being provided to compressors 754.

In some embodiments, a viscosity of the oil in VRF system 750 as well asoil levels in each of compressors 754 and accumulator 760 areestimated/predicted by an AI model. In this case, the AI model may takein inputs such as an operating speed of compressors 754, an ambienttemperature near VRF system 750, a discharge temperature and dischargepressure of discharge line 756, a suction pressure of suction line 752,a temperature of the refrigerant vapor and/or a temperature of other gaswithin VRF system 750, etc. Said inputs may be measured by sensors(e.g., temperature sensors, pressure sensors, etc.) throughout VRFsystem 750 and/or may be directly provided by components of VRF system750 (e.g., compressors 754 may directly output their operating speeds).

Based on the inputs, the AI model may predict characteristics of the oil(e.g., oil levels, oil viscosity, etc.) in VRF system 750. If thecharacteristics of the oil do not meet predefined thresholds (or someother constraitn), one or more corrective action may be initiated toaddress the failure of the oil characteristics to meet the predefinedthresholds. For example, if a viscosity of the oil in VRF system 750 istoo high or too low, a corrective action may include introducing new oilto VRF system 750 and/or completely replacing the oil within VRF system750 at a specific time. As another example, if an oil level in aspecific compressor 754 is too low, a corrective may be initiated tooperate the specific compressor 754, a specific valve 768, and/oraccumulator 760 such that the specific compressor 754 can obtain moreoil from accumulator 760 at a specific time that results in a relativelylow impact to efficiency of VRF system 750. The AI models and correctiveactions that can be initiated are described in greater detail below withreference to FIGS. 8-10 .

Systems and Methods for Oil Level and Viscosity Estimation

Referring generally to FIGS. 8-10 , systems and methods for estimatingand predicting oil characteristics in a VRF system are shown anddescribed, according to some embodiments. It should be appreciated thatthe description below is described with reference to a VRF system forsake of example only and should not be regarded as limiting. The systemsand methods described throughout FIGS. 8-10 can be similarly applied toa variety of systems that utilize oil (e.g., other building systems,vehicle systems, etc.) and are not meant to be limited to VRF systems.

The systems and methods described below can utilize artificialintelligence (AI) models to predict how characteristics of the oilchange over time based on a variety of inputs. The AI models utilize caninclude any appropriate type of AI model. For example, the AI models maybe or include long short-term memory (LSTM) models, other types ofrecurrent neural networks (RNNs), convolutional neural networks (CNNs),etc. A type of AI model to utilize can be selected based on, forexample, accuracy of a given AI model, what specific inputs/outputs areof consideration, user preferences, etc. In some embodiments, RNN modelssuch as LSTM models are preferred due to the time-series nature of oil.It should be noted that machine learning models may be referred toherein as synonymous with AI models.

The AI model can be trained to predict certain oil characteristics basedon a set of training data. The training data may be provided by avariety of sources. For example, a user may provide a set of inputsincluding a variety variables that can help the AI model to determinehow the oil is being utilized in the VRF system and a corresponding setof outputs based on actual measured operating states of the system or asimilar system. In this case, the inputs may include, for example, theinputs may include an operating speed of a compressor, an ambienttemperature near the compressors, a discharge temperature of thecompressor, a suction pressure of the compressor, a discharge pressureof the compressor, a gas temperature of gas in the compressor, etc. Asdefined herein, a discharge temperature of a compressor can refer to atemperature measure of a superheated refrigerant vapor in the VRFsystem, a suction pressure can refer to an intake pressure generated bythe compressor during operation, and a discharge pressure can refer to apressure generated on an output side of the compressor. The outputs mayinclude, for example, oil viscosity, an oil level of a compressor, anoil level of an accumulator, etc. The AI model can then be trained usingthe inputs and corresponding outputs to predict values of the outputsbased on inputs. Of course, said inputs and outputs are given for sakeof example and are not meant to be limiting on possible inputs to the AImodel.

In some embodiments, a simulation model is utilized to generate thetraining data used to train the AI model. Training data generated usingthe simulation model may be used separately or in addition to trainingdata gathered from other sources (e.g., from measured states of anactual system). The simulation model can be constructed to simulatechanges in an oil return system over time based on a variety ofconditions. In other words, the simulation model can be constructed todigitally mimic operation of an oil return system. States of thesimulation model (e.g., oil level, oil viscosity, energy consumption,etc.) can be manipulated to generate training data representing a widevariety of conditions. The simulation model may be executed multipletimes to generate training data representing evolution of the systemover time under a variety of different loads, using different buildingdevices, under different weather/environmental conditions, at differenttimes, etc. In terms of a VRF system, the simulation model may be, forexample, a closed loop functional mock-up unit (FMU) model that maytypically be used to operate the VRF system if no AI model is used.Advantageously, by utilizing a simulation model, large amounts oftraining data can be generated in shorter periods of time as compared tooperating an actual system over time to generate training data.Moreover, the simulation model can be executed to generate training datarepresenting fringe scenarios (e.g., dangerously high loads, dangerousoperating conditions, device faults, etc.) without subjecting an actualsystem to conditions that may be dangerous and may disrupt comfort ofoccupants in a real building.

Once trained based on a set of training data, the AI model can predictoil characteristics based on the inputs. For example, the AI model maypredict a viscosity of the oil, an oil level in a compressor, and an oillevel in an accumulator. Based on the predicted oil characteristics, adetermination can be made whether the predicted oil characteristicsadhere to predefined thresholds (and/or other constraints) on values ofthe characteristics. The predefined thresholds can include anylimitations on the values such as, for example, threshold values thevalues of the characteristics must be above or below, ranges ofacceptable values of the characteristics, etc. If the values of the oilcharacteristics meet the predefined thresholds, the VRF system cancontinue standard operation. However, if the values of the oilcharacteristics do not meet the predefined thresholds, a correctiveaction can be generated and initiated. Corrective actions, as definedherein, can refer to any action taken to address one or more oilcharacteristics not meeting some predefined constraint(s)/threshold(s).For example, corrective actions may be or include generating andtransmitting a notification to a user device, scheduling a technician toperform maintenance on the VRF system and/or to replace the oil,generating control signals and operating building equipment (e.g., VRFdevices) based on the control signals, disabling certain buildingdevices, automatically injecting new oil into the VRF system, loggingthe threshold violation(s) to a database, etc. A corrective action toinitiate can be determined based on a variety of factors such as whatthreshold is violated, an amount the threshold was violated (e.g., adifference between an actual value of the oil characteristic and athreshold value), user preferences, and/or any other applicableconsideration. Violation of a threshold on oil characteristics can alsoindicate a deficiency of the oil in that the oil is deficient of somedesired property (e.g., a desired viscosity). As described herein, aviolation of a threshold can refer to when a value (e.g., a predictedvalue) is above the threshold in the case of a maximum value threshold,or is below the threshold in the case of a minimum value threshold.

Referring now to FIG. 8 , a block diagram of an oil managementcontroller 800 for predicting characteristics of oil is shown, accordingto some embodiments. In particular, oil management controller 800 maypredict characteristics of oil in a VRF system. However, oil managementcontroller 800 can be applied to a variety of other systems/devices(e.g., other HVAC systems, car systems, etc.) that require oil toproperly operate. In some embodiments, oil management controller 800and/or components therein are incorporated in BMS controller 366 asdescribed with reference to FIGS. 3-4 and/or another controller. In someembodiments, oil management controller 800 is used to operate someand/or all of the VRF systems described throughout FIGS. 7A-7B.

Oil management controller 800 is shown to include a communicationsinterface 808 and a processing circuit 802. Communications interface 808may include wired or wireless interfaces (e.g., jacks, antennas,transmitters, receivers, transceivers, wire terminals, etc.) forconducting data communications with various systems, devices, ornetworks. For example, communications interface 808 may include anEthernet card and port for sending and receiving data via anEthernet-based communications network and/or a Wi-Fi transceiver forcommunicating via a wireless communications network. Communicationsinterface 808 may be configured to communicate via local area networksor wide area networks (e.g., the Internet, a building WAN, etc.) and mayuse a variety of communications protocols (e.g., BACnet, IP, LON, etc.).

Communications interface 808 may be a network interface configured tofacilitate electronic data communications between oil managementcontroller 800 and various external systems or devices (e.g., equipment822, sensors 820, a user device 824, etc.). For example, oil managementcontroller 800 may receive equipment feedback from equipment 822 viacommunications interface 808.

Processing circuit 802 is shown to include a processor 804 and memory806. Processor 804 may be a general purpose or specific purposeprocessor, an application specific integrated circuit (ASIC), one ormore field programmable gate arrays (FPGAs), a group of processingcomponents, or other suitable processing components. Processor 804 maybe configured to execute computer code or instructions stored in memory806 or received from other computer readable media (e.g., CDROM, networkstorage, a remote server, etc.).

Memory 806 may include one or more devices (e.g., memory units, memorydevices, storage devices, etc.) for storing data and/or computer codefor completing and/or facilitating the various processes described inthe present disclosure. Memory 806 may include random access memory(RAM), read-only memory (ROM), hard drive storage, temporary storage,non-volatile memory, flash memory, optical memory, or any other suitablememory for storing software objects and/or computer instructions. Memory806 may include database components, object code components, scriptcomponents, or any other type of information structure for supportingthe various activities and information structures described in thepresent disclosure. Memory 806 may be communicably connected toprocessor 804 via processing circuit 802 and may include computer codefor executing (e.g., by processor 804) one or more processes describedherein. In some embodiments, one or more components of memory 806 arepart of a singular component. However, each component of memory 806 isshown independently for ease of explanation.

Memory 806 is shown to include a training data collector 810. Trainingdata collector 810 can collect training data used to train an artificialintelligence model from one or more training data sources 818.Specifically, training data collector 810 can obtain training dataassociated with characteristics of oil in a VRF system. In someembodiments, training data collector 810 transmits queries to trainingdata sources 818 to obtain the training data. In some embodiments,training data collector 810 may passively receive training data fromtraining data sources 818 without needing to actively request thetraining data.

Training data sources 818 can include any source of data that can storeand/or provide training data to training data collector 810. Forexample, training data sources 818 may be or include a user device(e.g., a laptop, a desktop computer, a mobile device, a tablet, etc.)that can provide a stored training data set to training data collector810. As another example, training data sources 818 may be or include adatabase (e.g., a cloud database) that stores data associated withutilizing a standard VRF plant model with additional outputs of oillevel, oil viscosity, etc. In said example, the VRF plant model may bethe standard model used to operate the VRF system. In this way, thetraining data can include measurements from the VRF system in actualoperation along with measurements of the oil characteristics.

In some embodiments, training data collector 810 utilizes a simulationmodel to generate some or all of the training data used by modelgenerator 812 to generate an AI model. The simulation model can modelhow an actual system may operate under various conditions (e.g., weatherconditions, heating/cooling loads, device limitations, etc.) and how oilin the system may be consumed and/or otherwise change over time. In thisway, training data collector 810 may not need to retrieve training datafrom training data sources 818 and instead can generate the trainingdata within oil management controller 800. In some embodiments, thesimulation model is hosted by a third party controller/device/system(e.g., a cloud computing system) which can provide the training datagenerated as a result of running the simulation model to oil managementcontroller 800. In any case, the simulation model can be used/executedto generate a variety of training data representing various operatingconditions of a system utilizing oil in shorter periods of time ascompared to waiting for an actual system to generate training datathrough operation. Moreover, the simulation model can be executed togenerate training data illustrative of fringe scenarios that may bedangerous for an actual system to operate under purely for the sake ofgenerating training data.

Based on the obtained training data, training data collector 810 cancombine the collected training data into a training data set and providethe training data set to a model generator 812. Based on the trainingdata set, model generator 812 can generate an AI model that models oilcharacteristics over time. Specifically, model generator 812 can trainthe AI model to predict oil characteristics based on specified inputs.For example, model generator 812 may train the AI model to predictvalues of oil viscosity, oil level in one or more compressors, and oillevel in an accumulator based on inputs of compressor speeds, an ambienttemperature, a discharge temperature, a suction pressure, a dischargepressure, and a gas temperature.

The AI model generated by model generator 812 can be any of a variety ofAI model structures. In some embodiments, the AI model is an RNN modelsuch as an LSTM model. In this case, for the RNN to properly work on theVRF system, an original FMU plant model can be utilized to generateenough simulation data for RNN model to analyze and be trained based on.With training time increasing, the final RNN model may have much closerfunction as the original plant model. Some advantages of using RNNmodels in particular is that they may be faster and have a higherstability as compared with the FMU plant model. Further, the trained RNNmodel may reduce the influence the oil return and improve the efficiencyof the VRF system. With particular regard to LSTM models, an LSTM modelis an artificial RNN used for deep learning. LSTM models can classifyand process entire sequences of time series data and can makepredictions even with lags of unknown duration between important eventsin a time series. An LSTM model generated by model generator 812 mayinclude various structures depending on implementation. For example, anLSTM model generated by model generator 812 may include one sequenceinput layer, one drop out layer, two fully connected layers, and twoLSTM layers.

In some embodiments, the AI model is a CNN model. In this case, the CNNmodel may include, for example, an input layer, multiple hidden layers(e.g., rectified linear unit layers, pooling layers, fully connectedlayers, normalization layers, etc.), an output layer, etc. In someembodiments, the AI model follows some other artificial intelligencemodel architecture. Example architectures of the AI model are describedin greater detail below with reference to FIGS. 9A and 9B.

Model generator 812 may utilize a variety of training techniques togenerate the AI model. For example, model generator 812 may utilize astochastic gradient descent with momentum approach, an adaptive momentestimation approach, a root mean square propagation approach, etc. Withspecific regard to the root mean square propagation approach, modelgenerator 812 may utilize a root mean squared error (RMSE) to measurehow accurate model predictions are to the training data provided bytraining data collector 810. Specifically, model generator 812 maymonitor the RMSE over time based on the following equation:

RMSE=√{square root over ((Y _(pred,t) −Y _(test,t))² )}

where Y_(pred,t) is a previous prediction of the AI model for a variableY at a time step t, and Y_(test,t) is an actual value of the variable Yas indicated by the training data at time step t. The calculation of(Y_(pred,t)-Y_(test,t))² can be performed for each time step t=1. . . nwhere n is a total number of predictions. Each difference can then beaveraged together. During the training process, model generator 812 canrefine the AI model to reduce the RMSE. Example experiments of trainingan AI model are described in detail below with reference to FIGS. 11Aand 11B.

Model generator 812 can provide the generated AI model to a predictiongenerator 814. Prediction generator 814 can use the AI model to generatepredictions of oil characteristics over time. In order to generate saidpredictions, prediction generator 814 can operate to obtain values ofinputs required by the AI model from a variety of sources. For example,prediction generator 814 may obtain equipment feedback from equipment822, measured variables from sensors 820, and/or any other appropriatesource of input values.

Equipment 822 can be or include any devices that can provide values ofinputs needed by the AI model. For example, in a VRF system, equipment822 may include compressors, a heat exchanger, an accumulator, etc. Moreparticularly, if the AI model requires a compressor speed as an input,equipment 822 may include one or more compressors that can provide anoperating speed as equipment feedback to prediction generator 814.

Sensors 820 may be or include a variety of sensors that can measurevalues of inputs (i.e., variables) that are required by the AI model.For example, sensors 820 may include pressure sensors that measure asuction pressure and/or a discharge pressure. As another example,sensors 820 may include temperature sensors that measure an ambienttemperature, a discharge temperature, and/or a gas temperature.

Based on the AI model and the obtained input values, predictiongenerator 814 can generate oil characteristic predictions by passing theobtained input values through the AI model. As a result of passing theobtained input values through the AI model, the AI model can outputvalues of one or more oil characteristics (e.g., oil viscosity, oillevels in compressors, an oil level in an accumulator, etc.). In thisway, characteristics of the oil in the VRF system can be estimatedwithout the need for additional sensors to measure the oilcharacteristics.

In some embodiments, prediction generator 814 generates predictionsregarding oil at multiple stages within a VRF system. For example,prediction generator 814 may generate a prediction of viscosity of anoil-refrigerant mixture outputted by compressors and may predict aseparate viscosity of just the oil after the oil is separated from theoil-refrigerant mixture. In said example, predicting the viscosity ofthe oil-refrigerant mixture and the viscosity of just the oil may bebeneficial in determining how the oil may benefit from certaincorrective actions (e.g., an amount of oil to add/remove from the VRFsystem). By generating predictions regarding oil at multiple stageswithin the VRF system, oil deficiencies can be predicted and trackedover time throughout the VRF system rather than at a single point of theVRF system.

Prediction generator 814 can provide the predictions of the oilcharacteristics to a corrective action generator 816. Corrective actiongenerator 816 can analyze the predicted oil characteristics to determineif any corrective actions should be initiated and what correctiveactions to initiate. As defined above, a corrective action can refer toany action taken to address an oil characteristics not meeting somepredefined threshold(s). Corrective actions may include, for example,distributing notifications/alerts to user device 824 to indicate to auser that certain oil characteristics are violating the predefinedthresholds, operating equipment 822 in oil return control, disablingequipment 822, automatically scheduling a maintenance activity to beperformed on equipment 822, logging the threshold violation in adatabase, etc. The predefined threshold(s) can be defined by a user,provided by a manufacturer, estimated based on operating states ofequipment in the VRF system, etc. For example, a manufacturer may definea range of acceptable oil viscosity levels that compressors should beoperated with. In this case, overly viscous oil and/or oil with lowviscosity may result in more rapid deterioration of the compressors. Asanother example, a user may define a minimum oil level thresholddefining a lowest acceptable amount of oil in an accumulator. As shouldbe appreciated, thresholds defined for oil characteristics can beobtained from a variety of sources (e.g., manufacturers, users, based onpredictions, etc.) and can include a variety of limitation types (e.g.,ranges, threshold values, exact values to which characteristics shouldbe equal, etc.).

As a more specific example, consider a scenario where the AI modelpredicts values of oil characteristics including an oil level in acompressor, an oil level in an accumulator, a viscosity of the oil,and/or a viscosity of an oil-refrigerant mixture flowing through acompressor. In the example, corrective action generator 816 maydetermine whether the oil levels in the compressor and the accumulatorare above a first and second minimum threshold value, respectively. Ifthe oil level in the compressor is low, corrective action generator 816may determine the compressor should be operated in an oil return controlto retrieve oil from an external source (e.g., an indoor VRF system)and/or from the accumulator. As defined herein, oil return control mayindicate an operational mode in which the compressor runs at a highspeed to bring oil back from an outdoor system. Oil return control canresult in high energy consumption and may result in requiredheating/cooling being temporarily put on hold while the compressoroperates to retrieve oil. If the oil level in the accumulator is belowthe second minimum threshold value, corrective action generator 816 maydetermine that more oil should be added to the VRF system as the oillevel is too low. Addition of oil may result from operating thecompressors in oil return control, may include a user manually addingnew oil to the VRF system, etc.

Likewise, if corrective action generator 816 determines a viscosity ofthe oil or of the oil-refrigerant mixture is outside a predefined range,corrective action generator 816 may determine that oil should be addedor removed from the VRF system. Specifically, if the viscosity of theoil-refrigerant mixture is above a maximum bound of the range,corrective action generator 816 may determine oil should be removed fromthe VRF system (i.e., reducing an amount of oil in the oil-refrigerantmixture) to lower the viscosity. If the viscosity of the oil-refrigerantmixture is below a minimum bound of the range, corrective actiongenerator 816 may determine oil should be added to the VRF system (i.e.,increasing an amount of oil in the oil-refrigerant mixture) to increasethe viscosity. With regard to the oil itself, if the viscosity of theoil is above a maximum bound of the range, corrective action generator816 may determine oil should be added to the VRF system to lower theviscosity if new oil is expected to be less viscous as compared toexisting oil. If the viscosity of the oil is below the minimum bound,corrective action generator 816 may determine oil should be removed toincrease the viscosity.

In some embodiments, corrective action generator 816 compares outputs ofthe AI model over time to determine if certain oil characteristics areapproaching a threshold violation and will thereby include a deficiency.In this case, corrective action generator 816 may compare the values ofthe oil characteristics outputted by the AI model to previous outputtedvalues of the oil characteristics. If a particular oil characteristic istrending towards violating a threshold, corrective action generator 816may preemptively initiate a corrective action prior to the violationoccurring. For example, if a viscosity of the oil is increasing overtime and, based on a current trend, will exceed a maximum bound onviscosity within an upcoming time period, corrective action generator816 may initiate a corrective action prior to the viscosity of the oilexceeding the maximum bound. Advantageously, preemptive initiation ofcorrective actions can ensure that an amount of time that equipment(e.g., compressors) is operated under conditions associated withviolations of oil characteristic thresholds is reduced. Reducing saidamount of time can likewise reduce degradation of the equipment, reduceenergy consumption, and can provide other benefits.

In some embodiments, corrective action generator 816 predicts times toinitiate certain corrective actions to reduce an impact on equipment 822and/or other devices/systems. For example, corrective action generator816 may predict a time to initiate oil return control in order to reducea negative impact on heating/cooling loads required by a building. Asdescribed above, operating a compressor under oil return control canresult in required heating/cooling being temporarily postponed for aduration of the oil return control. Accordingly, corrective actiongenerator 816 can predict a time when the impact on requiredheating/cooling may be reduced (e.g., minimized). As another example, ifnew oil is required to be introduced to the system, corrective actiongenerator 816 can predict a time to temporarily disable equipment 822such that a new oil can be safely added to the system by an individual.

Corrective action generator 816 can utilize a variety of techniques topredict times at which to initiate certain corrective actions. Forexample, corrective action generator 816 may track certain variablesover time and identify a lower range of values that may result in lowamounts of disruption to a system. As a specific example, correctiveaction generator 816 may identify a range of values associated with lowheating/cooling needs such that an impact on environmental conditionswithin a building will be reduced. Based on the identified range ofvalues, corrective action generator 816 may track actual heating/coolingneeds over time and, in response to identifying a time period whereactual heating/cooling needs are within the identified range, caninitiate a corrective action during said time period. In this way,corrective action generator 816 is effectively predicting a time periodwhere initiating a corrective action results in a low overall impact.

In some embodiments, corrective action generator 816 can operate as astandard equipment controller in the case where no corrective actionsare needed (e.g., if oil viscosity and oil levels are at appropriatevalues). In other words, corrective action generator 816 can generatecontrol signals for equipment 822 in order to operate equipment 822 toaffect some variable state or condition (e.g., temperature, humidity,etc.) within a building. In some embodiments, corrective actiongenerator 816 may be configured to set boundary conditions for equipment822 based on the predictions provided by prediction generator 814. Forexample, corrective action generator 816 may set a maximum speed ofcompressors based on a prediction of oil viscosity. In said example, ifthe viscosity is within an appropriate range and is not approaching aviolation of the range, corrective action generator 816 may generatecontrol signals to operate the compressors at a higher speed as the oilviscosity is appropriate.

As a result of initiating some corrective action, any violations ofthresholds for oil characteristics can be addressed. This can ensurethat an amount of time during which equipment 822 is operating underconditions associated with some oil characteristic violation is reduced(e.g., minimized). Overall, in a VRF system, initiating the correctiveactions using predictions based on the AI model can save hardware cost,reduce an influence of oil return, and improve efficiency of the VRFsystem, among other benefits.

Referring now to FIG. 9A, an illustration of a recurrent neural network(RNN) structure 900 is shown, according to some embodiments.Specifically, RNN structure 900 can illustrate the structure of an RNNmodel (e.g., an LSTM model) that can be generated and utilized as the AImodel described above with reference to FIG. 8 .

RNNs are a class of artificial neural networks where connections betweennodes form a directed graph along a temporal sequence. In the case of aVRF system, the RNN model represented by RNN structure 900 can begenerated using simulation data collected based on an FMU plant model.As training time increases, the RNN model can have closer and closerfunction with the original plant model for the VRF system.

RNN structure 900 illustrates a condensed network structure and how thecondensed network structure can be “unfolded” to illustrate how RNNstructure 900 operates over a temporal sequence. Specifically, thecondensed (“folded”) structure and the unfolded structure areequivalent, with the unfolded structure illustrating usage of the RNNmodel over a temporal sequence in greater detail.

RNN structure 900 is shown to include an input represented as x whichmay be a vector including inputs required by the RNN model. For a VRFsystem, the input vector x may include, for example, a compressor speed,an ambient temperature, a discharge temperature, a suction pressure, adischarge pressure, a gas temperature, etc. A weight vector U can beapplied to x and a result provided to a hidden layer vector h.Similarly, a weight vector V can be applied to a hidden layer vector ofa previous time step. Based on the weighted inputs and the weightedvalues of the previous hidden layer vector, a function can be applied todetermine a corresponding output which, after a weight vector W isapplied, can result in an output o. This process can be repeated foreach time step of a temporal sequence. In other words, a new inputvector x_(t) can be obtained for a time step t and, based on x_(t), aprevious state h_(t-1), and corresponding weight vectors U, V, and W, anoutput vector o_(t) can be determined for time step t.

As a result of incorporating RNN structure 900 in the RNN modelgenerated and used by oil management controller 800, predictions of theRNN model can be modified over time as a result of previous time steps.As oil characteristics change over time a result of changing conditions(e.g., changing environmental conditions, operating conditions, etc.),utilizing the RNN model in particular can be useful due to the uniqueability of the RNN model to account for changes over a temporalsequence, as opposed to being limited by an original training process assome other neural network architectures are.

Referring now to FIG. 9B, an illustration of a neural network (NN)architecture 950 is shown, according to some embodiments. NNarchitecture 950 can describe a general architecture that may beutilized by the AI model described above with reference to FIG. 8 for aVRF system (e.g., VRF system 600). Specifically, NN architecture 950 canillustrate how a neural network can generate a set of outputs based on aset of inputs related to the VRF system. it should be noted, however,that NN architecture 950 is provided purely for sake of example of aneural network architecture that can be utilized and is not meant to belimiting on neural network architectures that can be utilized by the AImodel described with reference to FIG. 8 .

NN architecture 950 is shown to receive a compressor speed, an ambienttemperature, a discharge temperature, a suction pressure, a dischargepressure, and a gas temperature as inputs. Each input can be associatedwith a particular input node of an input layer in NN architecture 950.In other words, a number of nodes in the input layer may correspond to anumber of actual inputs as a one-to-one relationship. It should beappreciated that the inputs shown in FIG. 9B are provided purely forsake of example. NN architecture 950 can be modified to account forvarious different inputs depending on implementation. For example, ifgas temperature is not accounted for as an input, the input layer mayonly include five input nodes.

NN architecture 950 is also shown to include a hidden layer includinghidden nodes. In NN architecture 950, the hidden layer is shown toinclude a single layer including a number of hidden nodes that isequivalent to the number of input nodes of the input layer. However, itshould be noted that, according to various embodiments, the hidden layercan include one or more layers including varying numbers of hidden nodesthat may or may not correspond to a number of input nodes. For example,in a convolutional neural network architecture, NN architecture 950 mayinclude multiple hidden layers (e.g., multiple convolutional layers)that have varying numbers of hidden nodes. Moreover, the nodes of eachlayer need not necessarily connect to every node of adjacent layers asshown in FIG. 9B.

In NN architecture 950, a weight W can be applied with regard toconnections between two nodes. In some embodiments, each connectionbetween nodes includes a particular value for a particular connection.For example, a weight between input node 1 of the input layer and hiddennode 1 of the hidden layer may be different from a weight between inputnode 1 and hidden node 2 of the hidden layer. In some embodiments,various connections between nodes may be associated with the sameweight. For example, in an LSTM-specific architecture, the weightsassociated with connections between input nodes and hidden nodes may bethe same.

Based on each weighted value incoming to a particular node, a functioncan be applied to determine a composite value of the node. For example,for hidden node 1 of NN architecture 950, a function can be applied tothe weighted input values incoming to the node to determine a compositevalue of hidden node 1. Composite values of each node in a particularlayer to determine outputs of the particular layer. The outputs of theparticular layer can correspond with inputs to a subsequent layer alongwith weights between the particular layer and the subsequent layer. Thisprocess can be repeated for each layer until an output layer is reached.

NN architecture 950 is also shown to include an output layer includingoutput nodes. A number of output nodes in the output layer cancorrespond to desired outputs of the NN model on a one-to-one basis.With particular regard to the VRF system, the outputs may include oilviscosity, oil-refrigerant mixture viscosity, an oil level of one ormore compressors, and an oil level of an accumulator. Accordingly, afirst, second, and third output node can correspond with the oilviscosity, the oil level of the one or more compressors, and the oillevel of the accumulator, respectively. With regard to NN architecture950, a composite value of output node 1 can correspond to oil viscosity,a composite value of output node 2 can correspond to the oil level ofthe one or more compressors, and a composite value of output node 3 cancorrespond to the oil level of the accumulator. In this way, by simplyproviding the input values to NN architecture 950, predicted values ofthe outputs can be generated.

Referring now to FIG. 10 , a flow diagram of a process 1000 formonitoring oil characteristics using an AI model is shown, according tosome embodiments. Process 1000 can leverage the AI model to predictvalues of the oil characteristics and can initiate corrective actions ifsaid values do not meet predefined thresholds. While process 1000 isdescribed primarily with reference to a building system (e.g., a VRFsystem), process 1000 can be applied to a variety of systems thatinclude components/devices that require oil for proper operation. Forexample, process 1000 can be applied to VRF systems, other HVAC systems,car systems, etc. In some embodiments, some and/or all steps of process1000 are performed by oil management controller 800 as described withreference to FIG. 8 .

Process 1000 is shown to include obtaining training data describing arelationship between conditions affecting oil used by building equipmentof a building and characteristics of the oil (step 1002). The buildingequipment can include a variety of devices that can affect a variablestate or condition of the building and utilizes oil for properoperation. For example, the building equipment may include compressors,AHUs, other subplants, etc. The training data can be obtained from avariety of sources. For example, the training data may be obtain viadirect input from a user, by accessing a database (e.g., a clouddatabase) storing historical information associated with operation ofthe building equipment, using training data provided by a manufacturerof the building equipment, etc. In some embodiments, step 1002 includesgenerating the training data using a simulation model. If a simulationmodel is used, the simulation model can generate some and/or all of thetraining data obtained in step 1002. The simulation model can bestructured to account for various aspects of the system including thebuilding equipment such as, for example, how much oil is used by devicesof the building equipment during operation, how external weatherconditions and/or other ambient conditions affect the system, variousheating/cooling loads of the building, etc. During generation of thetraining data, variables associated with the simulation model can bemanipulated to generate training data representing a variety ofscenarios. Using the simulation model in step 1002 can result in agreater amount of training data being available in a shorter amount oftime as compared to gathering data based on actual operation of thebuilding equipment. Moreover, using the simulation model in step 1002can help obtain training data describing fringe cases that may not betypically included in training data collected based on actual deviceoperation. In some embodiments, step 1002 is performed by training datacollector 810.

Process 1000 is shown to include generating an artificial intelligence(AI) model based on the training data to model the characteristics ofthe oil (step 1004). The AI model generated in step 1004 can be of avariety of different AI models such as, for example, an RNN model (e.g.,an LSTM model), a CNN model, etc. The AI model can be generated toassociate the conditions affecting the oil and the oil characteristicsthemselves. Specifically, the AI model may be trained to associatecertain inputs (e.g., compressor speed, ambient temperature, dischargetemperature, suction pressure, discharge pressure, gas temperature,etc.) with certain outputs (e.g., oil viscosity, an oil level in acompressor, an oil level in an accumulator, etc.). In some embodiments,step 1004 may include training weights associated with connectionsbetween nodes of the AI model to account for relationships between theconditions and the characteristics of the oil. In some embodiments, step1004 is performed by model generator 812.

Process 1000 is shown to include using the AI model to generatepredictions of the oil characteristics over time based on a set of modelinputs (step 1006). As described above in step 1004, the AI model can betrained to associate certain inputs with certain outputs. Accordingly,once trained, the AI model can utilize values of the inputs to predictcorresponding values of the outputs (i.e., the oil characteristics). Insome embodiments, step 1006 is performed by prediction generator 814.

Process 1000 is shown to include determining whether the predictionsviolate any constraints (step 1008). In some embodiments, theconstraints are predefined constraints that define acceptable values ofthe oil characteristics. For example, the constraints may includethreshold values that values of the oil characteristics should beabove/below, acceptable ranges of values that the values of the oilcharacteristics should be within, etc. Specifically, the constraints maybe thresholds that should not be violated. As a particular example, aconstraint for oil viscosity may be defined as a range of acceptablevalues that the oil viscosity can be within. In said example, if thepredicted oil viscosity is above a maximum value of the range or below aminimum value of the range, a violation may be identified. If thepredicted oil characteristics do not violate any constraints andtherefore has no deficiencies (step 1008, “NO”), process 1000 may repeatstarting at step 1006. In this case, a new set of predictions can begenerated for a subsequent time step such that the oil characteristicscan be monitored/tracked over time. However, if a constraint violationis identified (step 1008, “YES”), process 1000 may proceed to step 1010.In some embodiments, a single constraint violation will result inprocess 1000 proceeding to step 1010. In some embodiments, multipleconstraint violations (e.g., 2 constraint violations, 3 constraintviolations, etc.) may be required for process 1000 to proceed to step1010. In some embodiments, step 1008 includes at least partiallyaccounting for a severity of particular constraint violations indetermining whether to proceed to step 1010. For example, an oilviscosity exceeding a maximum viscosity by 0.001 Pascal-seconds mayrequire some other constraint to also be violated for process 1000 toproceed to step 1010 whereas the oil viscosity exceeding the maximumviscosity by 1 Pascal-second may be independently sufficient for process1000 to proceed to step 1010. In some embodiments, step 1008 may includepredicting whether an oil characteristic will violate a constraintwithin an upcoming time period, and if so, can cause process 1000 toproceed to step 1010 to preemptively address the anticipated violation.In some embodiments, step 1008 is performed by corrective actiongenerator 816.

Process 1000 is shown to include determining a corrective action basedon what oil characteristic(s) violated constraints (step 1010). In otherwords, the corrective action can be determined to address the particularoil characteristic(s) that are violating one or moreconstraints/thresholds. For example, if an oil viscosity violates aconstraint (e.g., a maximum allowable viscosity), the corrective actiondetermined may be to transmit a notification to a user device to notifythe user that servicing of the building equipment may be necessary toreduce the oil viscosity. As another example, if an oil level of acompressor is below a minimum threshold, the corrective actiondetermined in step 1010 may be to operate the compressor in oil returncontrol to retrieve more oil (e.g., from an accumulator, from an indoorVRF system, etc.). In some embodiments, step 1010 includes determining aspecific time and/or time period for the corrective action to occur. Todetermine the specific time and/or time period, step 1010 may includemonitoring conditions (e.g., operating conditions of devices, ambientconditions, etc.) of the system to determine a time at which a lowestimpact on cost, heating/cooling efficiency, etc. may occur. In someembodiments, if the corrective action is transmitting a notification orif the constraint violation is severe, the determined time and/or timeperiod may be a soonest possible time (e.g., immediately). In someembodiments, step 1010 is performed by corrective action generator 816.

Process 1000 is shown to include initiating the corrective action (step1012). By initiating the corrective action determined in step 1010, theone or more constraint/threshold violations identified in step 1008 canbe addressed. In this way, an overall amount of time during which theone or more constraint/threshold violations are active can be reduced.Reducing an amount of time during which constraints/thresholds areviolated can help reduce degradation of the building equipment, reducecosts (e.g., energy costs), and can increase overall safety of thesystem, among other benefits. In some embodiments, if step 1010 includesdetermining when the corrective action should be performed, step 1012can include initiating the corrective action at the determined time. Insome embodiments, step 1012 is performed by corrective action generator816.

Experimental Results

Referring generally to FIGS. 11A-12C, results of an example experimentare shown, according to some embodiments. The example experiment ofFIGS. 11A-12C is provided for illustrative purposes only and is notintended to be limiting on the present disclosure, but rather to showthe practicality of utilizing an AI model in predicting oilcharacteristics. The AI model referenced below throughout FIGS. 11A-12Cis an RNN model trained for purposes of predicting oil characteristics.

Referring now to FIGS. 11A and 11B, a pair of graphs illustratingresults of a training process of an AI model for an example experimentare shown, according to some embodiments. FIG. 11A is shown to include agraph 1100 illustrating changes in RMSE based on a number of iterationsof the example training process. FIG. 11B is shown to include a graph1150 illustrating changes in loss based on the number of iterations. Theexample training process associated with FIGS. 11A and 11B utilized tenclosed loop test cases from a VRF model-based definition (MBD) oilviscosity plant model with approximately 4000 seconds allocated for eachtest case as training data. To determine accuracy of the AI model, asingle test data set from the VRF MBD oil viscosity plant model withapproximately 4000 seconds allocated for each test case was used forcomparison.

Graph 1100 is shown to include a series 1102. Series 1102 can illustratehow the RMSE associated with the AI model changes as a result ofadditional iterations of the training process. Specifically, series 1102illustrates a generally decreasing trend as the number of iterationsincreases. In other words, increasing the number of iterations canimprove accuracy of the AI model. It should be noted that series 1102represents a smoothed curve of the data points of RMSE collected at eachiteration.

Graph 1150 is shown to include a series 1152. Series 1152 can illustratehow a loss associated with the AI model changes over time based on anumber of iterations. In this case, loss describes how inaccuratepredictions of the AI model are with a loss of 0 indicating a particularprediction is equivalent to actual measurements. As is apparent fromseries 1152 and series 1102, accuracy of the AI model for predicting oilcharacteristics increases based on the number of iterations.

Referring generally to FIGS. 12A-12C, graphs illustrating oilcharacteristic predictions of the AI model trained in the exampleexperiment of FIGS. 11A-11B are shown, according to some embodiments. Inthe example experiment, the AI model took in inputs of compressor speed,ambient temperature, discharge temperature, suction pressure, dischargepressure, and gas temperature to produce predicted outputs of oilviscosity, an oil level in a compressor, and an oil level of anaccumulator.

Referring specifically to FIG. 12A, a graph 1200 illustratingpredictions of the AI model regarding an oil level of a compressor isshown, according to some embodiments. Graph 1200 is shown to include aseries 1202 illustrating predicted oil levels in the compressor overtime. In some embodiments, an objective of operational decisionsassociated with the compressors may be to maintain a relatively constantvalue of the oil level in the compressors to ensure steady and reliableoperation.

Referring now to FIG. 12B, a graph 1210 illustrating predictions of theAI model regarding an oil level of an accumulator is shown, according tosome embodiments. Graph 1210 is shown to include a series 1212illustrating predicted oil levels in the accumulator over time. Changesin series 1212 may result from, for example, changes in operation ofcompressors. Specifically, compressors may provide output anoil/refrigerant mixture that is separated such that the oil of theoil/refrigerant mixture is gathered by the accumulator, which may resultin predicted increases of series 1212. Alternatively, compressors mayoperate to retrieve oil from the accumulator, which may result indecreases of series 1212.

Referring now to FIG. 12C, a graph 1220 illustrating predictions of theAI model regarding a viscosity of oil is shown, according to someembodiments. Oil viscosity may change based on external temperature, howcompressors utilize the oil, etc. Accordingly, the AI model can predicthow the oil viscosity changes over time based on how an overall systemutilizing the oil changes.

Utilizing AI for VRF System Management

Referring generally to FIGS. 13A-24 , systems and methods for utilizingAI for generating predictions regarding characteristics of VRF systemsare shown and described, according to some embodiments. In particular,the systems and methods described below illustrate how AI can beutilized to predict vibrations of compressors in VRF systems, faultconditions in VRF systems, and efficiency of motors within compressors.Based on the predictions of the AI, determinations can be made regardingwhether any characteristics of the VRF system and/or components thereinare operating beyond preferred operating bounds. As described herein, acorrective action can refer to any action taken to address somepredicted characteristic of a VRF system and/or one or more componentstherein being beyond predefined acceptable bounds (e.g., predefinedthresholds). For example, if an AI model predicts that a fault conditionexists for the VRF system, a corrective action that automaticallyschedules maintenance to fix the fault condition may be initiated. Asanother example, if an AI predicts that a rate of vibrations of acompressor is beyond a predefined maximum threshold (e.g., a maximumnumber of motion cycles per minute), a corrective action that can beinitiated may be to lower an input current being provided to thecompressor and/or to temporarily disable to the compressor.

As described herein, the term “AI” can be used to refer to a variety ofdifferent types of AI models used for generating predictions. Inparticular, the systems and methods described herein may leverage neuralnetworks to generate predictions associated with VRF systems. Forexample, recurrent neural networks (RNNs) such as long short-term memory(LSTM) networks may be used to generate predictions. Example structuresof LSTMs are described in greater detail below with reference to FIGS.25A and 25B. Of course, it should be appreciated that other types ofneural networks and/or different AI models can be utilized in generatingpredictions associated with VRF systems. For example, convolutionalneural networks (CNNs), multi-layer perceptrons, etc. can be utilized ingenerating predictions. In some embodiments, multiple AI models aretrained for generating specific predictions and can be tested for anaccuracy of predictions relative to a known data set. Based on thedetermined accuracy of each AI model, an AI model that generatespredictions that most closely match the known data set can be selectedand utilized.

As described in greater detail below, AI models utilized for generatingpredictions can be trained based on a variety of different data sources.In some embodiments, the AI models are trained based on real datagathered from one or more VRF systems in operation. In some embodiments,the AI models are trained based on data generated based on a simulatedenvironment. In this case, the simulated environment can be structuredto mimic an actual VRF system in operation. Advantageously, using asimulation for generating training data may result in a larger set oftraining data being generated than may otherwise be generated by a realVRF system in operation. In some embodiments, the AI models are trainedbased on a mix of training data generated by a simulation, gathered froman actual VRF system, and/or from some other source of training data.

By utilizing AI models to generate predictions associated with VRFsystems, issues within the VRF systems can be identified and addressedquickly. Moreover, the AI models can help accurately identify possibleissues within the VRF systems before they become a more serious issue(e.g., complete failure of a building device). In this way, the AImodels can

Compressor Vibration Prediction

Referring generally to FIGS. 13A-18 , systems and methods for managingvibrations of compressors in a VRF system are shown and described,according to some embodiments. However, it should be appreciated thatthe below description is not necessarily limited to compressors in a VRFsystem. The systems and methods described below can be applied to avariety of different compressors and/or different building equipment ina variety of systems (e.g., different HVAC systems).

Vibrations of compressors can result in rapid degradation of thecompressors. As a frequency and/or intensity of vibrations increases, acompressor may become increasingly susceptible to faults and otherissues that may require maintenance and/or complete replacement of thecompressor. However, complete elimination of vibrations may beunrealistic as compressors should vibrate to some extent duringoperation. As such, operation of the compressors should be balancedbetween reducing vibrations while still fulfilling loads and/or otherrequired needs of the VRF system.

As referred to herein, “compressors” may be used to generally refer to avariety of different types of compressors that can be utilized in VRFsystems. For example, VRF systems may include single rotary compressors,twin rotary compressors, scroll compressors, etc. In VRF systems, acompressor may be operated based on an alternating current (AC) providedby an inverter. Specifically, the inverter can change a frequency of theAC provided to the compressor to change a speed at which a motor of thecompressor rotates and can change an amplitude of the AC to change atorque applied by the motor.

As the AC provided by the inverter directly affects operation of thecompressor, a correlation between characteristics of the AC (e.g.,frequency and/or amplitude) and vibrations of the compressor can beidentified. For example, a frequency of the vibrations may increase as afrequency and/or intensity (i.e., amplitude) of the AC increases. Basedon identified relationships, an AI model can be trained to learn whatvalues of current characteristics (e.g., amplitude) can result inreduced vibrations while still satisfying heating/cooling loads and/orother requirements of the compressor. In some embodiments, and asdescribed in greater detail below, the AI model may specifically betrained to predict appropriate values of AC amplitude to modify a torqueapplied by the motor in order to manage (e.g., reduce) vibrations.

In some embodiments, the AI model structured for predicting appropriatecurrent values for a compressor utilizes noise as a proxy forvibrations. In this way, the AI model can be trained to predict anappropriate amplitude of the AC provided to the compressor that reducesan amount of noise produced by the compressor. Using noise as a proxyfor vibrations may be appropriate as a result of known relationshipsbetween noise and vibrations. In general, louder noises may indicateincreased vibrations whereas quieter noises may indicate fewervibrations. More particularly, a relationship between decibels (dBs) anda frequency of vibrations of the compressor in hertz (Hz) may exist. Forexample, a 1 dB increase in noise may be determined to be correlatedwith a 1 Hz increase in vibration frequency. Advantageously, using noiseas a proxy for vibrations may reduce overall costs as sensors fordirectly measuring vibrations may be significantly more expensive ascompared to sensors for measuring sound (e.g., standard audio sensors).

Referring now to FIG. 13A, a block diagram of a VRF system 1300 isshown, according to some embodiments. As should be appreciated, VRFsystem 1300, as shown in FIG. 13A, illustrates a portion of a larger VRFsystem that utilizes compressors to provided heating/cooling to abuilding. For example, VRF system 1300 may be a subsystem of VRF system600 as described with reference to FIGS. 6A and 6B.

VRF system 1300 is shown to include a converter 1304 and an inverter1306. Converter 1304 can receive alternating current (AC) power from anAC power source (not shown) and can convert the AC power to directcurrent (DC) power. Converter 1304 can provide the converted DC power toinverter 1306. Inverter 1306 may be an electronic modulator that changesa frequency of the received DC signal and outputs an AC signal to acompressor 1302. In particular, inverter 1306 can operate to manipulatethe frequency and/or amplitude of the AC signal such that the outputtedAC signal results in a motor 1308 of compressor 1302 operating at aspecific speed and torque.

In some embodiments, the frequency of the AC signal provided by inverter1306 is relative to a known amount of a fluid passing through compressor1302 and/or a known amount of the fluid that is expected to pass throughcompressor 1302. The frequency of the AC signal can be indicative of howquickly motor 1308 should rotate and/or is rotating, which may bemeasured in rotations per minute, as to ensure a proper amount of thefluid flows through compressor 1302. The frequency of the AC signal maythereby be determined based on heating/cooling loads of a buildingand/or other targets. For example, a higher required cooling load forthe building may result in a higher frequency of the AC signal, therebyresulting in motor 1308 rotating quicker to pass more fluid throughcompressor 1302. The frequency of the AC signal may be determined by acontroller, by inverter 1306 itself, based on feedback from compressor1302, etc.

To determine what amplitude (also referred to herein as intensity) ofthe AC signal to output, inverter 1306 may receive an AC amplitudesetpoint from a compressor vibration controller 1310. In someembodiments, compressor vibration controller 1310 utilizes an AI modelto predict what amplitude value (e.g., in volts) should be provided tomotor 1308 as to ensure compressor 1302 does not degrade too quickly asa result of vibrations. Based on the received AC amplitude setpoint,inverter 1306 can modulate the DC signal received from converter 1304 asto output an AC signal to compressor 1302 that matches (or nearlymatches) the AC amplitude setpoint provided by compressor vibrationcontroller 1310 as well as the desired frequency described above.Compressor vibration controller 1310 is described in greater detailbelow with reference to FIG. 16 .

VRF system 1300 is also shown to include compressor 1302. Compressor1302 can be any type of compressor (e.g., a single rotary compressor, atwin rotary compressor, a scroll compressor, etc.) used to compress afluid. Compressor 1302 can be structured to intake a fluid (e.g., a gas)via a suction line, compress the fluid, and output the compressed fluidvia a discharge line. As described above, compressor 1302 may includemotor 1308 that drives components of compressor 1302 to compress thefluid and move the fluid through compressor 1302. Motor 1308 can operatebased on the AC signal (electric current) received from inverter 1306.Specifically, the frequency of the AC signal can affect an operatingspeed of motor 1308.

In traditional systems, the electric current provided by inverter 1306may be a predetermined, stagnant value that does not account forvibrations of compressor 1302. These traditional systems may result incompressor 1302 becoming quickly degraded as compressor 1302 may bevibrating at dangerous frequencies and/or intensities. However, asdescribed in greater detail below with reference to FIG. 16 , compressorvibration controller 1310 can predict an appropriate value for theamplitude of the AC signal provided by inverter 1306 such thatvibrations of compressor 1302 can be managed (e.g., reduced). In thisway, inverter 1306 can consistently provide an electric current tocompressor 1302 that is determined respective of possible vibrations asto increase the longevity of compressor 1302.

Referring now to FIG. 13B, a block diagram of VRF system 1300 in greaterdetail is shown, according to some embodiments. Motor 1308 may be anytype of motor that operates based on an AC signal. For example, motor1308 may be a single-phase motor that operates based on a single-phasesource of power. As another example, and as shown in FIG. 13B, motor1308 may be a three-phase motor that operates based on a three-phasesource of power.

As described above with reference to FIG. 13A, inverter 1306 can beconfigured to provide an AC signal to motor 1308 based on the ACamplitude setpoint provided by compressor vibration controller 1310. Asshown in FIG. 13B, the signal provided to motor 1308 may be athree-phase signal including phases A, B, and C. In this case,compressor vibration controller 1310 may generate a prediction for thethree-phase signal needed to properly operate motor 1308. Each of thephases A, B, and C may correspond to a specific axis, namely the A-axis,B-axis, and C-axis, respectively.

In some embodiments, compressor 1302 may receive a DC signal as opposedto an AC signal. In this case, inverter 1306 may or may not be acomponent of VRF system 1300. For example, converter 1304 may includesome and/or all of the functionality of inverter 1306 for modulating asignal to provide to motor 1308. An intensity of the DC signal may bedetermined by compressor vibration controller 1310 and provided toconverter 1304 in order to affect a torque applied by motor 1308.However, a speed at which motor 1308 may be determined based on someother input value other than a frequency of the electric signal. Forexample, the rotational speed of motor 1308 may be directly provided tomotor 1308 by compressor vibration controller 1310 and/or some othercomputing device. However, compressor 1302/motor 1308 are describedherein as receiving an AC signal from inverter 1306 for ease ofexplanation and clarity.

FIG. 13B further illustrates the direct axis (d-axis) and the quadratureaxis (q-axis) of motor 1308. The d-axis and the q-axis may always be ata 90 degree angle relative to one another. In some embodiments, thethree-phase current provided by inverter 1306 may be separated for thed-axis and the q-axis. In this case and as described in greater detailbelow with reference to FIG. 16 , compressor vibration controller 1310may generate a first three-phase current prediction for the d-axis and asecond three-phase current prediction for the q-axis. In this way, motor1308 can be properly operated to compress a received fluid. Calculationsof the axial currents made by compressor vibration controller 1310 aredescribed in greater detail below with reference to FIGS. 14A-14C.

The three-phase current signal provided by inverter 1306 is shown toinclude phases A, B, and C. In some embodiments, compressor vibrationcontroller 1310 is configured to predict appropriate amplitude valuesfor each phase. In some embodiments, compressor vibration controller1310 is configured to predict a single amplitude value that applies tophases A, B, and C. It should be appreciated that using a singleamplitude value that applies to each phase may be less computationallyexpensive to determine as compared to determining three separateamplitude values.

Referring now to FIGS. 14A-14C, a set of graphs illustrating currentconversions are shown, according to some embodiments. FIGS. 14A-14Cillustrate relationships between currents that can be used by compressorvibration controller 1310 in generating predictions for what currentvalues to provide to a motor (e.g., motor 1308). Compressor vibrationcontroller 1310 can be configured to perform both forward and reverse dqcurrent conversions. The d-axis and q-axis of a motor can be associatedwith current values I_(dc) and I_(qc), respectively. In forward currentconversion, I_(dc) and I_(qc) can be determined by converting a detectedU-phase and W-phase observed current values I_(uc) and I_(wc) to valueson a virtual rotational coordinate dcqc-axis.

Reverse current conversion can relate estimated current values Î_(uc)and Î_(wc) used in UVW current capture processing. UVW current captureprocessing can include inversely transforming the value of the virtualrotational coordinate dcqc-axis to determine Î_(uc) and Î_(wc). AV-phase current Î_(vc) can additionally be determined via Kirchoff'slaw, as described in greater detail below. In the UVW current captureprocessing, I_(u) and I_(w) can be estimated in order to compensate forthe motor current information when detection is invalidated in a currentdetection condition setting processing. Estimated values I_(uc) andI_(wc) of I_(u) and I_(w) can be obtained by inversely converting to theUVW based on the value of the dcqc-axis. In addition, I_(vc) used forcalculation formula for I_(u). I_(w) estimation can be obtained fromKirchhoff s law.

Referring more particularly to FIG. 14A, a graph 1400 illustratingKirchhoff's law is shown, according to some embodiments. Graph 1400 isshown to include currents I_(v), I_(u), and I_(w) emanating from a pointa. Following Kirchoff's law, a sum of the current at point a is 0.Accordingly, Kirchoff's law holds that:

I _(v) =−I _(u) −I _(w)

This relationship can be used in both forward and reverse currentconversion as described in greater detail below with reference to FIGS.14A and 14B.

Referring now to FIG. 14B, a graph 1420 illustrating an αβ conversion isshown, according to some embodiments. In forward current conversion, anαβ conversion can be performed for currents I_(αc) and I_(βc) bycompressor vibration controller 1310. The αβ conversion can berepresented by the following equation:

$\begin{bmatrix}I_{\alpha c} \\I_{\beta c}\end{bmatrix} = {{\frac{2}{3}\begin{bmatrix}1 & {- \frac{1}{2}} & {- \frac{1}{2}} \\0 & \frac{\sqrt{3}}{2} & {- \frac{\sqrt{3}}{2}}\end{bmatrix}}\begin{bmatrix}I_{u} \\I_{v} \\I_{w}\end{bmatrix}}$

If the relationship of I_(v)=−I_(u)−I_(w) is substituted into the aboveequation by compressor vibration controller 1310, the followingrelationship can be identified:

$\begin{bmatrix}I_{\alpha c} \\I_{\beta c}\end{bmatrix} = {\begin{bmatrix}1 & 0 \\{- \frac{1}{\sqrt{3}}} & {- \frac{2}{\sqrt{3}}}\end{bmatrix}\begin{bmatrix}I_{uc} \\I_{wc}\end{bmatrix}}$

where I_(uc)=I_(u) and I_(wc)=I_(w). In this way, I_(αc) and I_(βc) canbe obtained using Kirchoff's law.

Referring now to FIG. 14C, a graph 1440 illustrating a dq conversion isshown, according to some embodiments. In graph 1440, θ_(dc) canrepresent an angle between the fixed coordinate α-axis and the virtualrotational coordinate dc-axis. In forward current conversion, the dqconversion can be represented by the following equation where I_(dc) andI_(qc) can be obtained by substituting I_(αc) and I_(βc):

$\begin{bmatrix}I_{dc} \\I_{qc}\end{bmatrix} = {\begin{bmatrix}{\cos\left( \theta_{dc} \right)} & {\sin\left( \theta_{dc} \right)} \\{- {\sin\left( \theta_{dc} \right)}} & {\cos\left( \theta_{dc} \right)}\end{bmatrix}\begin{bmatrix}I_{\alpha c} \\I_{\beta c}\end{bmatrix}}$

Values of I_(dc) and I_(qc) can be filtered and used for control of acompressor motor (e.g., motor 1308). To filter I_(dc) and I_(qc), afilter time constant (e.g., in milliseconds, in seconds, etc.) of thefilter can be switched according to a rotational speed of the motor. Insome embodiments, the rotational speed is divided into three regions,namely a low speed region, a medium speed region, and a high speedregion. For example, the low speed region may be defined as 0-99rotations per minute (RPM), the medium speed region may be defined as100-200 RPM, and the high speed region may be defined as 200+ RPM. Basedon an identified rotational speed, the filter time constant can beswitched respective of the observed current. In particular, the filtertime constant may be the largest in the low speed region and shortest inthe high speed region. In this way, as rotational speed increases, thefilter time constant can decrease to obtain more granular resolution ofcurrent.

With regard to reverse current conversion, a dq inverse transformationcan be performed by compressor vibration controller 1310 based on thefollowing dq inverse transformation equation:

$\begin{bmatrix}I_{\alpha c} \\I_{\beta c}\end{bmatrix} = {\begin{bmatrix}{\cos\left( \theta_{dc} \right)} & {- {\sin\left( \theta_{dc} \right)}} \\{\sin\left( \theta_{dc} \right)} & {\cos\left( \theta_{dc} \right)}\end{bmatrix}\begin{bmatrix}I_{dc} \\I_{qc}\end{bmatrix}}$

In this way, values of I_(αc) and I_(βc) can be obtained. Compressorvibration controller 1310 can further perform an αβ inversetransformation to obtain values of I_(uc) and I_(wc) using the followingequation:

$\begin{bmatrix}{\overset{\hat{}}{I}}_{uc} \\{\overset{\hat{}}{I}}_{wc}\end{bmatrix} = {\begin{bmatrix}1 & 0 \\{- \frac{1}{2}} & {- \frac{\sqrt{3}}{2}}\end{bmatrix}\begin{bmatrix}I_{\alpha c} \\I_{\beta c}\end{bmatrix}}$

Referring now to FIG. 15 , a graph 1500 a relationship between crankangle and torque for different types of compressors is shown, accordingto some embodiments. Specifically, graph 1500 illustrates a relationshipbetween a crank angle and a gas compression torque. As described indetail above with reference to FIGS. 13A and 13B, a torque applied by amotor (e.g., motor 1308) can affect vibrations of a compressor.Accordingly, a torque applied by the motor should be monitored andmaintained as to ensure excessive vibrations of the compressor areavoided.

Graph 1500 is shown to include a series 1502, a series 1504, and aseries 1506. Series 1502 is associated with torque measurements of asingle-rotary compressor, series 1504 is associated with torquemeasurements of a twin-rotary compressor, and series 1506 is associatedwith torque measurements of a scroll compressor all with respect to acrank angle of the motor. As should be appreciated based on graph 1500,each type of compressor may have a different relationship between crankangle and torque (and thereby vibrations). For example, thesingle-rotary compressor represented by series 1502 is shown to have ahigh peak torque with respect to the crank angle whereas the scrollcompressor represented by series 1506 has a fairly constant relationshipbetween the crank angle and torque. During a training process for an AImodel, compressor vibration controller 1310 can leverage theserelationships and generate different AI models for various types ofcompressors.

Referring now to FIG. 16 , a block diagram of compressor vibrationcontroller 1310 in greater detail is shown, according to someembodiments. Compressor vibration controller 1310 can be configured toutilize an AI model to generate predictions for values of current thatcan be used to operate compressor 1302 (and/or some other compressor)and reduce degradation of compressor 1302 due to vibrations. Compressorvibration controller 1310 can be implemented in a variety of ways. Insome embodiments, compressor vibration controller 1310 is a localcontroller of a building system. For example, compressor vibrationcontroller 1310 may be implemented on a desktop computer, a mobiledevice, a thermostat, and/or some other computing device/system local toa building. In some embodiments, compressor vibration controller 1310may be implemented as a component of a VRF system. For example,compressor vibration controller 1310 may be implemented as a componentof inverter 1306 as described with reference to FIG. 13A. In this case,inverter 1306 itself may be able to determine a frequency of a currentto provide to compressor 1302. In some embodiments, compressor vibrationcontroller 1310 is implemented via some other computing device/systemsuch as by a cloud computing system.

Compressor vibration controller 1310 is shown to include acommunications interface 1608 and a processing circuit 1602.Communications interface 1608 may include wired or wireless interfaces(e.g., jacks, antennas, transmitters, receivers, transceivers, wireterminals, etc.) for conducting data communications with varioussystems, devices, or networks. For example, communications interface1608 may include an Ethernet card and port for sending and receivingdata via an Ethernet-based communications network and/or a Wi-Fitransceiver for communicating via a wireless communications network.Communications interface 1608 may be configured to communicate via localarea networks or wide area networks (e.g., the Internet, a building WAN,etc.) and may use a variety of communications protocols (e.g., BACnet,IP, LON, etc.).

Communications interface 1608 may be a network interface configured tofacilitate electronic data communications between compressor vibrationcontroller 1310 and various external systems or devices (e.g., inverter1306, sensors 1620, a user device 1622, etc.). For example, compressorvibration controller 1310 may provide AC amplitude setpoints to inverter1306 via communications interface 1608.

Processing circuit 1602 is shown to include a processor 1604 and memory1606. Processor 1604 may be a general purpose or specific purposeprocessor, an application specific integrated circuit (ASIC), one ormore field programmable gate arrays (FPGAs), a group of processingcomponents, or other suitable processing components. Processor 1604 maybe configured to execute computer code or instructions stored in memory1606 or received from other computer readable media (e.g., CDROM,network storage, a remote server, etc.).

Memory 1606 may include one or more devices (e.g., memory units, memorydevices, storage devices, etc.) for storing data and/or computer codefor completing and/or facilitating the various processes described inthe present disclosure. Memory 1606 may include random access memory(RAM), read-only memory (ROM), hard drive storage, temporary storage,non-volatile memory, flash memory, optical memory, or any other suitablememory for storing software objects and/or computer instructions. Memory1606 may include database components, object code components, scriptcomponents, or any other type of information structure for supportingthe various activities and information structures described in thepresent disclosure. Memory 1606 may be communicably connected toprocessor 1604 via processing circuit 1602 and may include computer codefor executing (e.g., by processor 1604) one or more processes describedherein. In some embodiments, one or more components of memory 1606 arepart of a singular component. However, each component of memory 1606 isshown independently for ease of explanation.

Memory 1606 is shown to include a training data collector 1610. Trainingdata collector 1610 can collect training data used to train anartificial intelligence model from one or more training data sources1618. Specifically, training data collector 1610 can obtain trainingdata associated with vibrations of compressor 1302.

The training data collected by training data collector 1610 can includeany relevant data that can be used to train an AI model to learnassociations between certain inputs and vibrations of compressor 1302.Alternatively or additionally, the training data may include informationindicative of a relationship between certain inputs and noise generatedby compressor 1302 if noise is used as a proxy for vibrations. Thetraining data may include values for inputs including, for example, anaxial error between an A-axis and a d-axis of motor 1308, a detectedq-axis current, a frequency of the AC signal provided by inverter 1306,measured noised near compressor 1302, etc. The training data can alsoinclude values of current characteristics (e.g., amplitude) such thatthe AI model can learn relationships between the inputs and outputs.

To gather the training data, training data collector 1610 may transmitqueries to training data sources 1618 to obtain the training data. Insome embodiments, training data collector 1610 may passively receivetraining data from training data sources 1618 without needing toactively request the training data. Training data sources 1618 caninclude any source of data that can store and/or provide training datato training data collector 1610. For example, training data sources 1618may be or include a user device (e.g., a laptop, a desktop computer, amobile device, a tablet, etc.) that can provide a stored training dataset to training data collector 1610. As another example, training datasources 1618 may be or include a database (e.g., a cloud database) thatstores data collected during operation of an actual VRF system. In thisway, the AI model can be trained based on data collected directly actualVRF devices in operation.

In some embodiments, training data collector 1610 utilizes one or moresimulation models to generate some or all of the training data used bymodel generator 1612 to generate the AI model. A simulation model (alsoreferred to as a “simulation” or “simulation framework” herein) cansimulate how an actual VRF system may operate under various conditionsand limitations (e.g., weather conditions, heating/cooling loads,intrinsic device limitations, etc.). The simulation model can accountfor relationships between components of the VRF system and how thecomponents may react to changing conditions. For example, the simulationmodel can model how compressor 1302 may operate to achieve certainrequired heating/cooling loads and how compressor 1302 vibrates as aresult.

By utilizing the simulation model, training data collector 1610 may notneed to retrieve training data from training data sources 1618 andinstead can generate the training data within compressor vibrationcontroller 1310. In some embodiments, the simulation model is hosted bya third party controller/device/system (e.g., a cloud computing system)which can provide the training data generated as a result of running thesimulation model to compressor vibration controller 1310. In any case,the simulation model can be used/executed to generate a variety oftraining data representing various operating conditions that can be usedto train an AI model.

Advantageously, the simulation model can generate a large amount of datain a shorter amount of time as compared to gathering training data froman actual VRF system in operation. In this way, compressor 1302 can beoperated based on decisions of an AI model in a shorter at an earliertime of time, which may increase the longevity of compressor 1302 asvibrations are managed sooner. Moreover, the simulation model can beused to generate training data for scenarios that may not frequentlyoccur during actual VRF system operation. For example, the simulationmodel may be able to generate training data representative of operatingcompressor 1302 under intense heating/cooling loads that may not occurfrequently during standard operation of an actual VRF system. In thisway, a large amount of training data representative of a variety ofcases can be generated and made available to model generator 1612 togenerate an accurate and representative model.

Based on the obtained training data, training data collector 1610 cancombine the collected training data into a training data set and providethe training data set to a model generator 1612. Based on the trainingdata set, model generator 1612 can generate an AI model that models arelationship between a set of inputs describing operating conditions ofcompressor 1302 and corresponding outputs describing amplitudes of ACsignals that can be provided to compressor 1302 to manage vibrations ofcompressor 1302. In some embodiments, model generator 1612 trains the AImodel to predict AC signal amplitudes that can be provided to motor 1308via inverter 1306 as to reduce noise generated by compressor 1302 whilestill satisfying required heating/cooling loads.

The AI model generated by model generator 1612 can be of any various AImodel architectures. For example, the AI model may be an RNN such as anLSTM network.

Inputs to the AI model generated by model generator 1612 can include avariety of inputs associated with operation of compressor 1302 and/ormotor 1308. For example, inputs to the AI model may include an axialerror (also referred to as an axial difference) between the d-axis andA-axis of motor 1308, a q-axis current detection value, an inverterfrequency indicative of a rotational speed of motor 1308, and a realand/or required noise level. In some embodiments, the rotational speedof motor 1308 (e.g., in rotations per minute (RPMs)) is used as an inputinstead of or in addition to the inverter frequency. Outputs of the AImodel can input target amplitude values of the AC signal (e.g., involts, in amps, etc.) to provide to compressor 1302 in order to manage atorque applied by motor 1308. Example illustrations of the AI modelsthat can be generated by model generator 1612 are described in greaterdetail below with reference to FIGS. 17A and 17B.

Model generator 1612 may utilize a variety of training techniques togenerate the AI model. For example, model generator 1612 may utilize astochastic gradient descent with momentum approach, an adaptive momentestimation approach, a root mean square propagation approach, etc. Withspecific regard to the root mean square propagation approach, modelgenerator 1612 may utilize a root mean squared error (RMSE) to measurehow accurate model predictions are relative to the training dataprovided by training data collector 1610. To monitor the RMSE over time,model generator 1612 may utilize the following equation:

RMSE=√{square root over ((Y _(pred,t) −Y _(test,t))² )}

where Y_(pred,t) is a previous prediction of the AI model for a variableY at a time step t, and Y_(test,t) is an actual value of the variable Yas indicated by the training data at time step t. The calculation of(Y_(pred,t)-Y_(test,t))² can be performed for each time step t=1. . . nwhere n is a total number of predictions. Each difference can then beaveraged together. During the training process, model generator 1612 canrefine the AI model to reduce the RMSE.

Model generator 1612 can provide the generated AI model to a predictiongenerator 1614. Prediction generator 1614 can use the AI model togenerate predictions for values of AC signal amplitude that reduce anamount of vibrations and/or noise generated by compressor 1302. In orderto generate said predictions, prediction generator 1614 can operate toobtain values of inputs required by the AI model from a variety ofsources. For example, prediction generator 1614 may obtain measuredvariables from sensors 1620, values of variables manually observed byusers via user device 1622, and/or any other appropriate source of inputvalues. With regard to specific inputs to the AI model, a rotationalspeed of motor 1308 may be observed by a user, directed calculated byprediction generator 1614 based on a known frequency being provided tomotor 1308, etc. In some embodiments, the axial difference between theA-axis and d-axis of motor 1308 is estimated by an observer (e.g., auser) by providing a small current to motor 1308 and analyzing feedbackfrom motor 1308 to estimate the axial difference. In some embodiments,the axial difference is measured by a sensor of sensors 1620. However,manual estimation may be preferred due to a price associated withsensors configured to measure the axial difference. In some embodiments,the q-axis feedback current associated with compressor 1302 is directlymeasured via a current sensor of sensors 1620. With respect to noise, areal noise level may be measured via audio sensors of sensors 1620and/or estimated by a user. If the AI model utilizes a required noiselevel as input, the required noise level may be provided by a user viauser device 1622, estimated/determined by prediction generator 1614 (oranother component of compressor vibration controller 1310), etc. In thiscase, the required noise level describes a predetermined noise value(e.g., in dB) that should be achieved by compressor 1302.

As a result of passing received values of inputs through the AI modelgenerated by model generator 1612, prediction generator 1614 candetermine an amplitude setpoint for the AC signal provided to compressor1302 by inverter 1306. In some embodiments, the AC amplitude setpointincludes distinct amplitudes for the current provided for the d-axis andthe current provided for the q-axis. In some embodiments, a single ACamplitude setpoint is determined as a result of passing the input valuesthrough the AI model. In this case, the single AC amplitude setpoint canbe applied to both the d-axis current and the q-axis current.

In some embodiments, prediction generator 1614 directly provides the ACamplitude setpoint to inverter 1306 (e.g., via communications interface1608). In some embodiments, prediction generator 1614 provides the ACamplitude setpoint to a corrective action generator 1616 of memory 1606.Corrective action generator 1616 can be configured to generate andinitiate one or more corrective actions based on the AC amplitudesetpoint. In some embodiments, one corrective action is to provide theAC amplitude setpoint to inverter 1306. In this case, the correctiveaction generated and initiated by corrective action generator 1616 maybe to operate inverter 1306 based on a control signal indicative of theAC amplitude setpoint. In this way, a torque applied by motor 1308 canbe adjusted. In some embodiments, another corrective action that may begenerated and initiated by corrective action generator 1616 is toprovide a notification to user device 1622 as to notify a user about theAC amplitude setpoint. Said notification may allow the user toappreciate how differing AC amplitudes affect vibrations and may allowthe user to take preemptive measures to ensure building equipment (e.g.,compressor 1302) avoids rapid degradation due to vibrations. In someembodiments, corrective action generator 1616 generates and initiatesother types of corrective actions such as, for example, schedulingmaintenance on compressor 1302 in response to determining that the ACamplitude setpoint needed to avoid rapid degradation is below athreshold value, thereby jeopardizing fulfilment of heating/coolingloads of the building.

Referring now to FIG. 17A, an illustration of a neural network (NN) 1700for predicting values of a current to provide to a compressor motor isshown, according to some embodiments. NN 1700 can illustrate an examplestructure of an AI model that can be generated by model generator 1612as described with reference to FIG. 16 . NN 1700 is shown to includeinput nodes in an input layer that correspond to a set of inputs.Specifically, NN 1700 is shown to include input nodes for an axial errorbetween the A-axis and d-axis of motor 1308, a q-axis current detectionvalue, an inverter frequency describing the frequency of the AC signaloutputted by inverter 1306, and a real noise level. Of course, it shouldbe appreciated that these inputs are provided purely for sake ofexample. Model generator 1612 can generate AI models that utilizedifferent inputs that can be used to estimate vibrations/noise of acompressor. In terms of outputs, NN 1700 is shown to include a separateoutput for a d-axis current amplitude and a q-axis current amplitude.Separate outputs for the d-axis and q-axis may be useful in moreprecisely operating compressor 1302. In some embodiments, NN 1700outputs a single current amplitude value that can be applied to both thed-axis current and the q-axis current. Outputting a single value mayreduce complexity of NN 1700, thereby improving processing efficiencywhen generating predictions.

As described above with reference to FIG. 16 , the axial error may bemeasured by a user and/or a sensor, the q-axis current detection valuemay be measured by a current sensor, and the inverter frequency may bemeasured, a known value, etc. As for the real noise level, the realnoise level may be measured by an audio sensor with a predefinedphysical proximity of compressor 1302 (e.g., within 10 feet, within 1foot, physically attached to compressor 1302, etc.). Based on the realnoise level, NN 1700 intrinsically predict vibrations of compressor 1302and can output an amplitude setpoint respective of the predictedvibrations. In this case, NN 1700 is being provided with feedbackregarding operation of compressor 1302. Specifically, as noise can beused as a proxy for vibrations, NN 1700 is effectively obtainingknowledge indicative of vibrations of compressor 1302. As such, NN 1700can leverage this information to predict AC signal amplitudes that avoiddangerous operating conditions. For example, if the noise level ofrelatively low (e.g., <50 dB, <20 dB, etc.), NN 1700 may output largeramplitudes (e.g., in volts or amps) as compared to if the noise level isrelatively high (e.g., >50 dB, >100 dB, >200 dB, etc.). In this way, NN1700 can change values of the amplitudes over time to achieveappropriate operating conditions for compressor 1302 that avoiddangerous operating conditions and vibrations.

Referring now to FIG. 17B, an illustration of a neural network (NN) 1750for predicting values of a current to provide to a compressor motor isshown, according to some embodiments. In some embodiments, NN 1750 issimilar to and/or the same as NN 1700 as described above with referenceto FIG. 17A. Accordingly, NN 1750 may be another example of an AI modelthat can be generated by model generator 1612 as described withreference to FIG. 16 . As shown in FIG. 17B, NN 1750 is shown to receivea required noise level as input as compared to the real noise level ofNN 1700. The required noise level may be a target value for noisegenerated by compressor 1302. In some embodiments, the required noiselevel functions as a threshold value such that the noise generated bycompressor 1302 is below the threshold. In particular, the requirednoise level may be a noise level (e.g., in dB) that is known to beassociated with vibrations that are determined (e.g., by a user) not toplace compressor 1302 in dangerous operating conditions. For example,the required noise level may be 50 dB which is expected to maintainvibrations of the compressor 1302 under a rate of 30 Hz.

In order to use required noise level as an input, NN 1750 may be trainedbased on a known correlation between noise and vibrations. In this way,NN 1750 can adjust the outputted current amplitudes based on otherinputs (e.g., the inverter frequency which is known to be associatedwith an amount of fluid passing through compressor 1302) relative to theknown correlation. In some embodiments, model generator 1612 generatesan AI model that utilizes both the real noise level and the requirednoise level as inputs.

Referring now to FIG. 18 , a flow diagram of a process 1800 forpredicting an AC signal amplitude to provide to a compressor using an AImodel is shown, according to some embodiments. The AC signal amplitudemay be an amplitude value (e.g., in volts, in amps, etc.) that ispredicted to result in decreased vibrations of a compressor while stillfulfilling needed heating/cooling loads and/or other requirements of abuilding. In some embodiments, the amplitude of the AC signal affects atorque applied by a motor of the compressor. Specifically, increasingamplitudes may relate to increasing torque applied by the motor. In someembodiments, some and/or all of the steps of process 1800 are performedby compressor vibration controller 1310 as described with reference toFIGS. 13 and 16 .

Process 1800 is shown to include obtaining training data describing arelationship between conditions affecting operation of a compressor andnoise generated by the compressor (step 1802). In this case, noise canbe used as a proxy for vibrations of the compressor. The training datamay include values of, for example, an axial error between an A-axis anda d-axis of a motor of the compressor, a q-axis current detection value,a frequency of an AC signal provided by an inverter (which may representan amount of fluid passing through the compressor and thereby a requiredheating/cooling load), a real and/or required noise level, noisemeasurements, etc. The training data may be obtained from one or moretraining data sources such as, for example, a cloud database, a databaselocal to a building, a user, sensors in the building, etc. In someembodiments, step 1802 is performed by training data collector 1610.

Process 1800 is shown to include training an AI model for predictingamplitudes of an AC signal that affects a torque applied by a motor ofthe compressor based on the training data (step 1804). In someembodiments, larger AC signal amplitudes correspond with increasedtorque applied by the motor. However, as the torque applied by the motorincreases, vibrations of the compressor may increase which can bemeasured based on an amount of noise outputted by the compressor. The AImodel trained in step 1804 can learn what AC signal amplitudescorrespond with certain noise levels dependent on a set of input values.In some embodiments, a goal of the AI model is to learn what AC signalamplitudes reduce and/or minimize noise outputted by the compressorwhile still fulfilling necessary heating/cooling loads. Of course, somenoise/vibrations may be inevitable due to operation, however the AImodel can be trained to identify amplitudes that avoid excessivedegradation of the equipment while still fulfilling needs of a building.In some embodiments, step 1804 is performed by model generator 1612.

Process 1800 is shown to include obtaining data associated withoperation of the compressor (step 1806). In this case, the data may beassociated with inputs to the AI model trained in step 1804. Forexample, the data obtain in step 1806 may include frequencies of the ACsignal provided and/or to be provided to the compressor, an ambienttemperature, a real noise level near the compressor, etc. The data canbe obtained from a variety of sources such as, for example, a user,sensors, as feedback from equipment, etc. In some embodiments, step 1806is performed by prediction generator 1614.

Process 1800 is shown to include using the AI model to generate an ACamplitude setpoint based on the obtained data (step 1808). The AI modelcan generate the AC amplitude setpoint such that a requiredheating/cooling load (and/or some other requirement/need of a building)is satisfied and that corresponding noise produced by the compressor isreduced. In this way, the motor is operated to compress a certain amountof fluid while avoiding unnecessary degradation. Advantageously, usingthe AI model to generate the AC amplitude setpoint can reduce afrequency of situations where the compressor experiences unnecessarydegradation as a result of the motor applying unnecessary amounts oftorque. In some embodiments, the AC amplitude setpoint includes separateamplitude setpoints for a d-axis and a q-axis of the motor. In someembodiments, step 1808 is performed by prediction generator 1614.

Process 1800 is shown to include providing the AC amplitude setpoint toan inverter in order to operate the motor (step 1810). In someembodiments, the inverter generates the actual AC signal provided to thecompressor. In this way, the inverter can operate such that the ACsignal outputted by the inverter has an amplitude equal to (orapproximately equal to) the AC amplitude setpoint. As a result, themotor can operate to apply torque respective of the amplitude of the ACsignal. In some embodiments, step 1810 is performed by predictiongenerator 1614 and/or corrective action generator 1616.

VRF System Fault Condition Prediction

Referring generally to FIGS. 19-22 , system and methods for predictingfault conditions of a VRF system are shown and described, according tosome embodiments. Fault conditions can include any sort of fault thatcan raise costs and/or result in some other undesirable trait of the VRFsystem. For example, fault conditions can include refrigerant leakage,outdoor unit frost, clogging of an indoor fan, a dirty indoor filter, adirty heat exchanger, a dirty outdoor fan, motor demagnetization,compressor oil leakage, etc. Fault conditions may result in imperfectefficiency of compressor that can affect costs (e.g., operational costs)associated with the VRF system.

As described in greater detail throughout FIGS. 19-22 , an AI model canbe used to predict the fault conditions of components in VRF system. Inparticular, the AI model can be used to predict fault conditionsassociated with a compressor of the VRF system. For each different faultsituation, a percentage can be used to represent an amount the failureinfluences the VRF system. Accordingly, there may be two models (e.g.,two RNNs) for detailed fault classification. The first model can bestructured to output a fault classification describing what faultconditions, if any, exist in the VRF system. Effectively, the firstmodel can be structured to determine/predict what fault (if any) the VRFsystem and/or components therein are experiencing. The second model canbe structured to determine/predict a severity of each fault conditionusing a representative percentage and/or some other metric. The severityof a fault can effectively indicate a degree of influence that the faulthas on the VRF system. For example, an output of a refrigerant leakageindex of 0.5 (i.e., 50%) by the second model may indicate that 50% ofrefrigerant is being lost through leakage. As another example, an outputof an indoor fan clogging index of 0.9 by the second model may indicatethat an indoor fan is 90% clogged. In some embodiments, the first modeland the second model are combined into a single model. In this case, thesingle model may output a fault classification indicating faultconditions within the VRF system and a corresponding metric(s)representing a severity associated with each condition. In someembodiments, the second model may not be utilized. In this case, a faultclassification generated by the first model is utilized in determining acorrective action to initiate. In other words, if the second model isnot utilized, a corrective action may be initiated based on the faultclassification and irrespective of an amount the fault impacts the VRFsystem.

As should be appreciated, the systems and methods shown and describedbelow throughout FIGS. 19-22 can be applied to a variety of buildingsystems and may not necessarily be limited to VRF systems. For example,the systems and methods can be similarly applied to other HVAC systemsof a building.

Referring now to FIG. 19 , a block diagram of a VRF system 1900 isshown, according to some embodiments. VRF system 1900 can beillustrative of an example VRF system to which an AI model can beapplied to generate predictions of fault classifications. In particular,VRF system 1900 can illustrate sources of input values that may be usedin generating fault classifications via the AI model. In someembodiments, VRF system 1900 is similar to and/or the same as VRF system600 as described with reference to FIGS. 6A and 6B.

VRF system 1900 is shown to include an outdoor unit (ODU) 1902 and anindoor unit (IDU) 1904. ODU 1902 is shown to include a compressor 1906and a heat exchanger 1908. Compressor 1906 can receive electric currentI from an inverter via a compressor power line 1914. I_(inverter) may beany type of electric power signal such as, for example, AC. I_(inverter)can power compressor 1906. In some embodiments, compressor 1906 operatesa motor respective of a frequency and an amplitude of I_(inverter) asdescribed in greater detail above with reference to FIGS. 13A-18 .

Compressor 1906 can also receive a fluid (e.g., a refrigerant) from heatexchanger 1908. To obtain the fluid, compressor 1906 can suction thefluid from heat exchanger 1908 via a suction line 1922. The suction ofthe fluid can be associated with a suction pressure P_(s) which may bemeasured in a suitable pressure metric such as pascals. In someembodiments, P_(s) is measured by a pressure sensor associated withsuction line 1922.

As a result of suctioning the fluid via suction line 1922, compressor1906 can compress the fluid and output the compressed fluid to IDU 1904via discharge line 1916. The output of the fluid via discharge line 1916can be associated with a discharge pressure P_(d) and a dischargetemperature T_(d). In some embodiments, P_(d) and T_(d) are measured bya pressure sensor and a temperature sensor associated with dischargeline 1916, respectively.

A fan 1912 of IDU 1904 can receive the compressed fluid (e.g., acompressed refrigerant) via discharge line 1916 as well as an indoor fancurrent I_(fan,i) via an indoor fan power line 1918. Using thecompressed fluid, fan 1912 can operate to provide heating/cooling to aspace. Specifically, heat emitted by the compressed fluid can be pushedusing fan 1912 into the space. In some embodiments, IDU 1904 includesmultiple fans 1912 that provide heating/cooling to various locationswithin the space.

IDU 1904 can recirculate the fluid back to ODU 1902 via a return line1920. Specifically, the fluid can be provided back to heat exchanger1908. A fan 1910, powered by outdoor fan current I_(fan,o) via outdoorfan power line 1924, can provide air to heat exchanger 1908. In thisway, some heat may be dissipated from the fluid to an external space.Finally, the fluid can be provided back to compressor 1906 via suctionline 1922.

The variables shown in VRF system 1900 and as described above (e.g.,I_(fan,i), P_(s), P_(d), T_(d), etc.) can be measured by respectivesensors attached, inside, or otherwise associated with lines 1914-1924.Measurements of said variables can be provided back to a controller thatutilizes an AI model to identify faults associated with VRF system 1900.For example, the variables may be provided to VRF fault controller 2000as described in detail below with reference to FIG. 20 . As should beappreciated, the variables and components shown in FIG. 19 are providedpurely for sake of example. VRF system 1900 may include differentcomponents than as shown in FIG. 19 . Likewise, variables other than orin addition to those shown in FIG. 19 may be provided to the controller.

Referring now to FIG. 20 , a block diagram of a VRF fault controller2000 for predicting faults in a VRF system is shown, according to someembodiments. VRF fault controller 2000 can be used to generatepredictions regarding fault associated with a VRF system (e.g., VRFsystem 600 as described with reference to FIG. 16 , VRF system 1900 asdescribed with reference to FIG. 19 , etc.). VRF fault controller 2000can utilize one or more AI models to generate a fault classification anda corresponding severity of fault conditions identified by the faultclassification. In some embodiments, VRF fault controller 2000 isintegrated with other controllers described herein as a singlecontroller. For example, functionality of VRF fault controller 2000 maybe integrated with oil management controller 800 as described withreference to FIG. 8 and/or compressor vibration controller 1310 asdescribed with reference to FIG. 16 .

VRF fault controller 2000 is shown to include a communications interface2008 and a processing circuit 2002. Communications interface 2008 mayinclude wired or wireless interfaces (e.g., jacks, antennas,transmitters, receivers, transceivers, wire terminals, etc.) forconducting data communications with various systems, devices, ornetworks. For example, communications interface 2008 may include anEthernet card and port for sending and receiving data via anEthernet-based communications network and/or a Wi-Fi transceiver forcommunicating via a wireless communications network. Communicationsinterface 2008 may be configured to communicate via local area networksor wide area networks (e.g., the Internet, a building WAN, etc.) and mayuse a variety of communications protocols (e.g., BACnet, IP, LON, etc.).

Communications interface 2008 may be a network interface configured tofacilitate electronic data communications between VRF fault controller2000 and various external systems or devices (e.g., training datasources 2018, sensors 2020, a user device 2024, etc.). For example, VRFfault controller 2000 may provide notifications describing a faultclassification of equipment 2022 to user device 2024 via communicationsinterface 2008.

Processing circuit 2002 is shown to include a processor 2004 and memory2006. Processor 2004 may be a general purpose or specific purposeprocessor, an application specific integrated circuit (ASIC), one ormore field programmable gate arrays (FPGAs), a group of processingcomponents, or other suitable processing components. Processor 2004 maybe configured to execute computer code or instructions stored in memory2006 or received from other computer readable media (e.g., CDROM,network storage, a remote server, etc.).

Memory 2006 may include one or more devices (e.g., memory units, memorydevices, storage devices, etc.) for storing data and/or computer codefor completing and/or facilitating the various processes described inthe present disclosure. Memory 2006 may include random access memory(RAM), read-only memory (ROM), hard drive storage, temporary storage,non-volatile memory, flash memory, optical memory, or any other suitablememory for storing software objects and/or computer instructions. Memory2006 may include database components, object code components, scriptcomponents, or any other type of information structure for supportingthe various activities and information structures described in thepresent disclosure. Memory 2006 may be communicably connected toprocessor 2004 via processing circuit 2002 and may include computer codefor executing (e.g., by processor 2004) one or more processes describedherein. In some embodiments, one or more components of memory 2006 arepart of a singular component. However, each component of memory 2006 isshown independently for ease of explanation.

Memory 2006 is shown to include training data collector 2010. In someembodiments, training data collector 2010 is similar to and/or the sameas training data collector 1610 as described with reference to FIG. 16 .Training data collector 2010 can be configured to obtain training datafor use in training an AI model from training data sources 2018.Training data sources 2018 can include a variety of sources that canprovide training data to VRF fault controller 2000. For example,training data sources 2018 may include a cloud database, a databaseon-site of a building, a user device, building equipment, etc. In someembodiments, training data sources 2018 are similar to and/or the sameas training data sources 1618.

The training data collected by training data collector 2010 can includeany information relevant for training an AI model to predict what faultsmay exist in a VRF system and, in some cases, a severity of said faults(e.g., a percentage value of the fault with 0% representing no issueexists and 100% representing a catastrophic failure). For example, thetraining data may include values of compressor speed, ambienttemperature, discharge temperature, suction pressure, dischargepressure, indoor fan mode and VRF mode (e.g., heating or cooling),outdoor fan step, etc. The training data may also include faultclassifications associated with the values. In this case, the faultclassifications may be entered into the training data by users (e.g.,maintenance personnel, a building owner, etc.) respective of the values.Alternatively or additionally, the fault classifications may beautomatically entered into the training data based on equipment feedbackindicating when certain devices have failed and/or are otherwise infault states. In some embodiments, the fault classifications are enteredinto the training data via some other source of fault classifications.

In some embodiments, training data collector 2010 utilizes a simulationframework to generate some and/or all of the training data necessary totrain the one or more AI models. In this case, the simulation frameworkcan execute simulations of test cases representing operation of a VRFsystem by using a closed loop functional mock-up unit (FMU) model of theVRF system. The simulation framework can model how devices degrade overtime and what faults may arise as a result. The simulation framework canbe executed for a variety of different loads, over varying durations,with different equipment, etc. In this way, a comprehensive collectionof training data can be obtained that can be used in training the AImodel to learn how to identity faults within the VRF system.Advantageously, the simulation framework can ensure that the AI modelhas enough training data to accurately model dynamics within the VRFsystem and how building devices are affected as a result. Simulationtraining data can be used instead of or in addition to training datagathered from training data sources 2018.

Training data collector 2010 can provide the collected training data asa training data set to a model generator 2012. In some embodiments,model generator 2012 is similar to and/or the same as model generator1612 as described with reference to FIG. 16 . Model generator 2012 canutilize the training data obtained from training data collector 2010 totrain one or more AI models for predicting characteristics of faultconditions. Specifically, model generator 2012 may train a first AImodel to output a fault classification respective of a set of inputsdescribing operation of the VRF system and a second AI model to output aseverity (e.g., a percentage value) of faults identified by the first AImodel have on the VRF system. In some embodiments, model generator 2012generates a single AI model that outputs a fault classification andcorresponding severities of each fault condition identified in the faultclassification. In some embodiments, model generator 2012 generates asingle AI model that only outputs a fault classification. An example ofan AI model that outputs a fault classification is described in greaterdetail below with reference to FIG. 21 .

A fault classification outputted by the one or more AI models candescribe faults in the VRF system. For example, the faultclassifications may include textual strings such as “refrigerantleakage,” “indoor fan clogged,” “outdoor unit frost,” “dirty indoorfilter,” “dirty heat exchanger,” “dirty outdoor fan,” “motordemagnetization,” “compressor oil leakage,” “imperfect efficiency ofcompressor,” etc. As another example, the fault classification mayindicate a presence of certain fault conditions using binary variables(e.g., 0 or 1) representing whether a particular fault condition exists.The fault classification can be predicted by the one or more AI modelsbased on inputs describing the VRF system. Specifically, the one or moreAI models may learn correlations between values of inputs variables suchas compressor speed, ambient temperature, discharge temperature, suctionpressure, discharge pressure, indoor fan mode, outdoor fan step, etc.and corresponding a fault classification. As a particular example, an AImodel may learn, based on the training data, that if the suctionpressure is above a particular threshold value (e.g., 20 pounds persquare inch (PSI), 30 PSI, etc.), there may be refrigerant leakagewithin the VRF system. The AI model may be further configured to predicta percentage value of how much refrigerant is leaking from the VRFsystem. In the example, the AI model may predict 50% of refrigerant isbeing lost due to leakage based on inputs such as compressor speed,discharge temperature, etc.

The one or more AI models generated by model generator 2012 can be anytype of AI model. For example, the one or more AI models may be RNNs(e.g., LSTM models), convolutional neural networks, multi-layerperceptrons, feed forward neural networks, etc. In particular, the oneor more AI models may be RNNs due to a high speed and reliability ofRNNs. More particularly, LSTMs may be utilized due to an ability ofLSTMs to process entire sequences of time-series data and makepredictions, even with lags of unknown duration between important eventsin the time-series. As a specific example structure, one of the AImodels generated by model generator 2012 may have one sequence inputlayer, one bidirectional LSTM layer, one fully connected layer, onesoftmax layer, and one classification layer. In this way, the AI modelcan be used to generate a fault classification and can learnbidirectional long-term dependencies between time steps of time-seriesor sequence data. These dependencies can be useful for the network tolearn from the complete time-series at each time step.

In some embodiments, model generator 2012 generates an individual AImodel for each fault condition. In this case, each AI model can betrained to predict whether a specific fault condition is present in theVRF system. Generating individual AI model may be increase accuracy forpredicting whether certain fault conditions exist. In particular,generating individual AI models can avoid overlapping data for multiplefault conditions which can affect generation of models. In someembodiments, if model generator 2012 generates individual AI models forfault conditions, model generator 2012 may combine each individual modelinto a composite AI model that accounts for multiple fault conditions.

Model generator 2012 can provided the one or more generated AI models toa prediction generator 2014. In some embodiments, prediction generator2014 is similar to and/or the same as prediction generator 1614. Usingthe one or more AI models, prediction generator 2014 can generatepredictions of what fault conditions, if any, are affecting the VRFsystem and, in some embodiments, a severity of the fault conditions. Togenerate the predictions, prediction generator 2014 can obtain valuesinputs required by the one or more AI models. Specifically, predictiongenerator 2014 may obtain measured variables from sensors 2020 andequipment feedback from equipment 2022. Sensors 2020 can include one ormore sensors configured to measure certain characteristics of the VRFsystem. For example, sensors 2020 may include temperature sensors,pressure sensors, etc. that measure values of P_(d), P_(s), T_(d), anambient temperature, etc. Equipment 2022 can include any equipment ofthe VRF system. For example, equipment 2022 may include ODUs, IDUs, etc.The equipment feedback may include inputs required by the one or more AImodels such as, for example, an indoor fan mode, an outdoor fan step, acompressor speed, a discharge temperature, a suction pressure, etc. Insome embodiments, sensors 2020 are components of equipment 2022 and/orare otherwise associated with equipment 2022.

Prediction generator 2014 can pass the received values of inputs throughthe one or more AI models to generate predictions associated with faultcharacteristics of the VRF system. Prediction generator 2014 can providesaid predictions to a corrective action generator 2016. Correctiveaction generator 2016 can be configured to initiate a variety ofdifferent corrective actions respective of what fault conditions areindicated by a fault classification included in the predictions and aseverity of the faults conditions. The corrective actions initiate bycorrective action generator 2016 can include any appropriate action toresolve one or more of the fault conditions identified in thepredictions. For example, corrective actions may include notifying auser regarding the fault conditions by providing a notification to userdevice 2024, generating and providing control signals to equipment 2022,temporarily disabling some VRF devices of equipment 2022, schedulingmaintenance for equipment 2022, etc.

In some embodiments, the corrective action(s) initiated by correctiveaction generator 2016 are based on the fault classification included inthe predictions and a severity of each fault. Some fault conditions mayrequire corrective actions that are associated with a higher cost (e.g.,$), require more time on the part of maintenance technicians, etc. ascompared to other corrective actions. For example, schedulingmaintenance for a particular building device (e.g., an IDU) may have ahigher associated cost as compared to providing a notification to userdevice 2024. Accordingly, corrective action generator 2016 may determinean impact a particular fault condition may have on the VRF system andinitiate a corrective action respective of the determined impact. Forexample, if a fault classification included in the predictions receivedfrom prediction generator 2014 indicates an indoor fan filter is 50%clogged, corrective action generator 2016 may determine an impact on theVRF system to be low and may initiate a corrective action for notifyinga user via user device 2024 regarding the clogging. However, if thefault classification indicates that a compressor of the VRF system is90% inefficient, corrective action generator 2016 may determine theinefficiency of the compressor will have a high impact on operationalcosts and thereby initiate a corrective action to perform maintenanceand/or replace the compressor.

To determine what corrective action to initiate, corrective actiongenerator 2016 may utilize a mapping between fault conditions withcorresponding severities and certain corrective actions. In particular,if the severities are given as percentages with 0% indicating no impactto the VRF system and 100% indicating severe impact to the VRF system,corrective action generator 2016 may initiate particular correctiveactions based on bounds of the severities. In some embodiments, multiplecorrective actions are initiated for single fault conditions. An exampleof a decision tree that can be utilized to determine what correctiveaction to initiate is provided below in Table 1.

TABLE 1 Corrective Action Decision Table Fault Severity ConditionPercentage Corrective Action Refrigerant  <50% Provide notification touser device Leakage ≥50% Schedule maintenance for VRF system Indoor Fan <80% Provide notification to user device Clogged ≥80% Provide controlsignals to indoor fan and provide notification user device Imperfect <25% No action Efficiency of ≥25% and <75% Schedule maintenance forcompressor Compressor ≥75% Schedule replacement of compressor

If a corrective action is initiated, a fault condition predicted toexist can be addressed. By utilizing the one or more AI models topredict a fault classification indicating certain fault conditions andcorresponding severities, appropriate corrective actions can beinitiated to address certain faults predicted by the one or more AImodels. Advantageously, corrective actions initiated to address certainfaults can be respective of an estimated severity of the fault. In thisway, expensive and/or time-consuming corrective actions can be avoidedfor faults that have a low impact on the VRF system.

Referring now to FIG. 21 , an illustration of a neural network (NN) 2100for predicting a fault classification of a VRF system is shown,according to some embodiments. NN 2100 can illustrate an examplestructure of an AI model that can be generated by model generator 2012as described with reference to FIG. 20 . Specifically, NN 2100 canrepresent a RNN structure for fault classification prediction.

NN 2100 is shown to include input nodes in an input layer thatcorrespond to a set of inputs. NN 1700 is shown to include input nodesof compressor speed, ambient temperature, discharge temperature, suctionpressure, discharge pressure, indoor fan mode, and outdoor fan step. Asshould be appreciated, the inputs shown in NN 2100 are provided purelyfor sake of example. The inputs to NN 2100 can be modified based on whatmeasurements can be gathered from the VRF system, what measurements arerelevant to generating a fault classification, etc.

An output of NN 2100 can include a fault classification that identifiesspecific faults that may exist within a VRF system. For example, NN 2100may identify fault conditions such as refrigerant leakage, clogging ofan indoor fan, frosting of an outdoor unit, a dirty indoor filter, adirty heat exchanger, a dirty outdoor fan, motor demagnetization,compressor oil leakage, imperfect efficiency of a compressor, etc.Similar to the inputs, the fault classification that can be outputted byNN 2100 can be modified. The fault classification that can be outputtedby NN 2100 can be tailored dependent on user preferences, devices withinthe system, what inputs are available to NN 2100, etc. Specifically, thefault classification can be tailored to only indicate whether a specificset of fault conditions exist within the VRF system.

Referring now to FIG. 22 , a flow diagram of a process 2200 forpredicting a fault classification for a VRF system using an AI model isshown, according to some embodiments. In some embodiments, some and/orall steps of process 2200 are performed by VRF fault controller 2000.

Process 2200 is shown to include obtaining training data associated withoperational conditions of a VRF system and fault conditions of the VRFsystem (step 2202). The training data can be obtained from a variety ofsources such as, for example, cloud databases, directly from users,equipment feedback, etc. The operational conditions included in thetraining data may include values of variables such as, for example,T_(d), P_(d), P_(s), an ambient temperature, a compressor speed, anoutdoor fan step (i.e., outdoor fan speed), an indoor fan speed, and aVRF mode (e.g., heating or cooling). The fault conditions included inthe training data may include any sort of fault condition that can beexperienced by the VRF system. For example, the fault conditions mayinclude refrigerant leakage, clogging of an indoor fan, frosting of anoutdoor unit, a dirty indoor filter, a dirty heat exchanger, a dirtyoutdoor fan, motor demagnetization, compressor oil leakage, imperfectefficiency of a compressor, etc. In some embodiments, each faultcondition included in the training data also includes an estimatedseverity (e.g., a percentage value, a value on a 1 to 10 scale, etc.).Estimated severities may be manually estimated by users, automaticallybased on changing operational costs indicated by the training data, etc.In some embodiments, the training data is time-series data such thatrelationships between changes in operational conditions and certainfault conditions can be identified. In some embodiments, step 2202 isperformed by training data collector 2010.

Process 2200 is shown to include training an AI model for predicting afault classification for the VRF system based on the training data (step2204). The AI model trained in step 2204 may be any type of AI modelsuch as an RNN. The fault classification predicted by the AI model canindicate whether one or more fault conditions exist within the VRFsystem and corresponding severities of any existing faults. In someembodiments, two AI models are trained in step 2204 such that a first AImodel predicts a fault classification indicating what, if any, faultconditions exist based on input data and a second AI model predicts theseverities of the fault conditions that do exist. An example of the AImodel that can be generated in step 2204 is described above withreference to FIG. 21 . In some embodiments, step 2204 is performed bymodel generator 2012.

Process 2200 is shown to include obtaining data associated withoperation of the VRF system (step 2206). The data obtained in step 2206can include values of inputs required by the one or more AI modelsgenerated in step 2204. For example, the data may include values ofT_(d), P_(d), P_(s), an ambient temperature, a compressor speed, anoutdoor fan step, an indoor fan speed, and a VRF mode. The data can beobtained from a variety of sources such as sensors, equipment feedback,from users, etc. In some embodiments, step 2206 is performed byprediction generator 2014.

Process 2200 is shown to include using the AI model to predict a faultclassification for the VRF system based on the obtained data (step2208). The fault classification can indicate what fault conditions, ifany, are identified to exist within the VRF system. The faultclassification may indicate existing fault conditions through anyappropriate method such as, for example, by including textual strings ofthe existing fault conditions, by including an array of binary variablesindicating the existence or nonexistence of certain fault conditions,etc. In some embodiments, step 2208 also includes using the AI model (ora second AI model) to predict severities of each existing faultcondition based on the obtained data. In some embodiments, step 2208 isperformed by prediction generator 2014.

Process 2200 is shown to include determining a corrective action basedon the fault classification outputted by the AI model (step 2210). Insome embodiments, the corrective action is determined based on whatfault conditions, if any, are identified by the fault classification. Insome embodiments, the corrective action is further determined based onan indicated severity of each fault condition identified by the faultclassification. The corrective action can include any action thataddresses the fault conditions indicated by the fault classification.For example, the corrective action may include notifying a userregarding the identified fault conditions and corresponding severities,scheduling maintenance/replacement for VRF equipment, generating andtransmitting control signals to the VRF equipment, logging the faultconditions and corresponding severities to a database, etc. In someembodiments, multiple corrective actions are determined in step 2210. Insome embodiments, step 2210 is performed by corrective action generator2016.

Process 2200 is shown to include initiating the corrective action (step2212). Step 2212 can include performing any steps/processes necessary toensure the corrective action is successfully executed. For example, ifthe corrective action is notifying a user, step 2412 may includegenerating a notification and communicating the notification using acommunications interface. As another example, if the corrective actionis disabling a VRF device, step 2412 may include generating a controlsignal that turns off the VRF device and providing the control signal tothe VRF device. In some embodiments, step 2212 is performed bycorrective action generator 2016.

Motor Efficiency Prediction

Referring generally to FIGS. 23-24 , systems and methods for predictingefficiency of a motor in a VRF system using an AI model are shown anddescribed, according to some embodiments. In some embodiments, thesystems and methods described below can be integrated with any and/orall of the systems and methods described above throughout FIGS. 8-22 .

Efficiency of a motor for a compressor can be defined based on an inputpower and an output power of the motor. Specifically, motor efficiencyη_(motor) can be given by the following equation:

$\eta_{motor} = \frac{\varphi_{{out},{motor}}}{\varphi_{{in},{motor}}}$

where φ_(out,motor) is an output power of the motor and φ_(in,motor) isan input power of the motor. In some embodiments, the motor is poweredbased on a received signal from an inverter. Efficiency of the inverterη_(inverter) can also be determined and can be defined based on thefollowing equation:

$\eta_{inverter} = \frac{\varphi_{{out},{inverter}}}{\varphi_{{in},{inverter}}}$

where φ_(out,inverter) is an output power of the inverter andφ_(in,inverter) is an input power of the inverter. In this case, theoutput power of the inverter should be the same (or nearly the same) asthe input power of the motor (i.e., φ_(out,inverter)=φ_(in,motor)).

Changes in motor efficiency may be an important index to detect faultsand inference reasons leading to efficiency reduction. The detection ofmotor efficiency can allow a system (e.g., a VRF system) to identifyabnormal information early (e.g., prior to catastrophic failure). Basedon detected changes in motor efficiency, operation of the motor can beadjusted accordingly. Specifically, detected changes in motor efficiencymay result in making offset compensations to the motor drive and/orstopping the machine entirely. As such, predictions of motor efficiencycan reduce overall hardware costs (e.g., operational costs, maintenancecosts, etc.) and can improve reliability of the motor drive.

As described in greater detail below, an AI model can be utilized topredict motor efficiency and, optionally, efficiency of an inverter todetect abnormal conditions of the motor drive system. The AI model usedto predict efficiency can be any type of AI model. In particular, the AImodel for efficiency prediction may be a linear RNN model due to acase-dependent nature of data describing efficiency. If a linear RNNmodel is used, the predicted efficiency may be an instantaneousefficiency of the motor.

The predicted motor efficiency may be indicative of a probability thatthe motor will fail. As the motor becomes more inefficient, theprobability that the motor will fail may increase. In this way, thepredicted motor efficiency can be used to predict when the motor isexpected to fail. In some embodiments, a threshold efficiency value canbe used to define failure of the motor. For example, the motor may beconsidered to have failed if the predicted efficiency is below 50%, 30%,20%, etc. In this way, a trend in changes to motor efficiency can beidentified and projected forward in time to predict a time when themotor efficiency is expected to fall below the threshold efficiency andthe motor is considered to have failed. Similar predictions can be madefor inverter efficiency and inverter failure.

Based on the predicted efficiency of the motor (and/or predicted timeswhen the motor is expected to fail), a determination can be maderegarding what corrective actions, if any, should be initiated toaddress the efficiencies. For example, if the motor is predicted to beoperating at full efficiency (i.e., 100% efficiency), a determinedcorrective action may be to log the predicted efficiency. As anotherexample, if the motor if predicted to be operating at 50% efficiency, acorrective action that schedules maintenance for the motor may bescheduled in order to increase the efficiency of the motor.

Referring now to FIG. 23 , a block diagram of a motor efficiencycontroller 2300 is shown, according to some embodiments. Motorefficiency controller 2300 can be configured to predict efficiency of amotor used in a building system. Specifically, motor efficiencycontroller 2300 can be configured to predict efficiency of a motor of acompressor in a VRF system. However, predictions of motor efficiency canbe similarly applied to motors in other systems (e.g., other HVACsystems). In some embodiments, functionality of motor efficiencycontroller 2300 is combined with one or more controllers describedherein (e.g., oil management controller 800, compressor vibrationcontroller 1310, and/or VRF fault controller 2000).

Motor efficiency controller 2300 is shown to include a communicationsinterface 2308 and a processing circuit 2302. Communications interface2308 may include wired or wireless interfaces (e.g., jacks, antennas,transmitters, receivers, transceivers, wire terminals, etc.) forconducting data communications with various systems, devices, ornetworks. For example, communications interface 2308 may include anEthernet card and port for sending and receiving data via anEthernet-based communications network and/or a Wi-Fi transceiver forcommunicating via a wireless communications network. Communicationsinterface 2308 may be configured to communicate via local area networksor wide area networks (e.g., the Internet, a building WAN, etc.) and mayuse a variety of communications protocols (e.g., BACnet, IP, LON, etc.).

Communications interface 2308 may be a network interface configured tofacilitate electronic data communications between motor efficiencycontroller 2300 and various external systems or devices (e.g., trainingdata sources 2318, sensors 2320, a motor 2322, a user device 2324,etc.). For example, motor efficiency controller 2300 may providenotifications describing motor efficiency predictions to user device2324 via communications interface 2308.

Processing circuit 2302 is shown to include a processor 2304 and memory2306. Processor 2304 may be a general purpose or specific purposeprocessor, an application specific integrated circuit (ASIC), one ormore field programmable gate arrays (FPGAs), a group of processingcomponents, or other suitable processing components. Processor 2304 maybe configured to execute computer code or instructions stored in memory2306 or received from other computer readable media (e.g., CDROM,network storage, a remote server, etc.).

Memory 2306 may include one or more devices (e.g., memory units, memorydevices, storage devices, etc.) for storing data and/or computer codefor completing and/or facilitating the various processes described inthe present disclosure. Memory 2306 may include random access memory(RAM), read-only memory (ROM), hard drive storage, temporary storage,non-volatile memory, flash memory, optical memory, or any other suitablememory for storing software objects and/or computer instructions. Memory2306 may include database components, object code components, scriptcomponents, or any other type of information structure for supportingthe various activities and information structures described in thepresent disclosure. Memory 2306 may be communicably connected toprocessor 2304 via processing circuit 2302 and may include computer codefor executing (e.g., by processor 2304) one or more processes describedherein. In some embodiments, one or more components of memory 2306 arepart of a singular component. However, each component of memory 2306 isshown independently for ease of explanation.

Memory 2306 is shown to include a training data collector 2310. In someembodiments, training data collector 2310 is similar to and/or the sameas training data collector 1610 as described with reference to FIG. 16and/or training data collector 2010 as described with reference to FIG.20 . Training data collector 2310 can obtain training data associatedwith predicting motor efficiency from training data sources 2318.Training data sources 2318 can include any appropriate source oftraining data such as cloud databases, local storage, user inputs,mobile devices, equipment feedback, etc. The training data can includetraining data that describes operation of motor 2322 under variousconditions. For example, the training data may include values of arotational speed of motor 2322, a power supplied by an inverter 2328 tomotor 2322, required heating/cooling loads, etc. Likewise, the trainingdata can include estimated efficiency values of the motor. Theefficiency values may be estimated by users, automatically based ondetected changes to costs associated with motor 2322 (e.g., changes inoperational costs where higher costs indicate lower efficiency), and/orestimated via some other process.

In some embodiments, training data collector 2310 utilizes a simulationframework to generate some and/or all of the training data. Thesimulation framework may model how motor 2322 operates based on variousoperating conditions (e.g., temperatures, heating/cooling duties, etc.).Likewise, the simulation framework can model how efficiency of motor2322 degrades over time as a result of operation. In this way, trainingdata representative of the operation and degradation of motor 2322 canbe generated and utilized for training the AI model.

Training data collector 2310 can provide the collected training data asa training data set to a model generator 2312. In some embodiments,model generator 2312 is similar to and/or the same as model generator1612 and/or model generator 2012. Using the training data, modelgenerator 2312 can generate an AI model (e.g., a linear RNN) that can beused to predict efficiency values of a motor in a VRF system (e.g., VRFsystem 600, VRF system 1900, etc.). In particular, the AI model mayoutput a percentage representative of a percentage of maximum efficiencyat which motor 2322 is operating. In this case, maximum efficiency canbe represented by a situation where an input power to motor 2322 isequal to an output power of motor 2322 (i.e.,φ_(out,inverter)=φ_(in,inverter)). In some embodiments, model generator2312 also generates the AI model such that the AI model also outputs aprediction of inverter efficiency. In this case, inverter efficiency canbe modeled similarly to motor efficiency and can be based on operatingconditions of inverter 2328 (e.g., required heating/cooling duties,ambient temperature, etc.). In some embodiments, model generator 2312generates separate AI models for predicting motor efficiency andinverter efficiency.

With regard to an AI model structured to predict efficiency of motor2322, the AI model may include inputs such as a rotational speed ofmotor 2322, a power provided to motor 2322 (e.g., by inverter 2328), anambient temperature, required heating/cooling loads, etc. Based on saidinputs, the AI model can output a prediction of motor efficiency (e.g.,as a percentage of maximum efficiency).

Model generator 2312 can provide the AI model to prediction generator2314. In some embodiments, prediction generator 2314 is similar toand/or the same as prediction generator 1614 and/or prediction generator2014. Prediction generator 2314 can utilize the AI model and receivedvalues of input variables to generate predictions of motor efficiency.In terms of input variables, prediction generator 2314 may receivevalues of measured variables from sensors 2320. Sensors 2320 can includeany type of sensor (e.g., temperature sensors, pressure sensors, etc.)that can measure values of inputs required by the AI model. For example,sensors 2320 may include temperature sensors that provide values of anambient temperature to prediction generator 2314. In some embodiments,sensors 2320 includes third party sources that provide needed inputvalues. For example, sensors 2320 may include a weather service thatprovides ambient temperature values.

Prediction generator 2314 can also obtain values of input variablesbased on equipment feedback from equipment 2326 including motor 2322 andinverter 2328. The equipment feedback can include values of variablessuch as, for example, a rotational speed of motor 2322, a power supplycurrent, voltage, and input power provided to motor 2322 by inverter2328, etc.

Using the obtained values of input variables, prediction generator 2314can pass the obtained input variables through the AI model to generatepredictions of motor efficiency. Said predictions of motor efficiencycan be provided to a corrective action generator 2316. In someembodiments, corrective action generator 2316 is similar to and/or thesame as corrective action generator 1616 and/or corrective actiongenerator 2016.

Corrective action generator 2316 can be configured to determine what, ifany, corrective actions should be initiated based on predictions ofmotor efficiency. In particular, corrective action generator 2316 candetermine whether actions should be taken to address reductions in motorefficiency. Corrective actions that can be initiated by correctiveaction generator 2316 may include, for example, alerting a user aboutthe predicted motor efficiency via user device 2324, adjusting operationof motor 2322 via control signals, disabling motor 2322, schedulingmaintenance/replacement of motor 2322, logging predicted values of motorefficiency to a database, etc.

To determine what corrective action to initiate, corrective actiongenerator 2316 may correspond certain efficiency ranges with certaincorrective actions. For example, correction action generator 2316 mayassociate efficiency values between 75% and 100% with a correctiveaction that notifies a user of the predicted efficiency, efficiencyvalues between 50% and 75% with a corrective action to adjust operationof motor 2322 via controls signals, and efficiency values between 0% and50% with a corrective action to schedule maintenance and/or replacementof motor 2322. In some embodiments, corrective action generator 2316utilizes some other determination for determining what corrective actionto initiate based on predicted efficiency values.

As a result of initiating a corrective action, inefficiency of motor2322 can be addressed. In this way, a frequency of situations wheremotor 2322 is operated under high inefficiency can be avoided. This canincrease reliability of a VRF system, reduce overall costs, and cansimplify general upkeep of the VRF system.

Referring now to FIG. 24 , a flow diagram of a process 2400 forpredicting an efficiency of a motor in a VRF system using an AI model isshown, according to some embodiments. It should be appreciated thatwhile process 2400 is described with reference to a motor of a VRFsystem, process 2400 can be similarly applied for generating efficiencypredictions associated with an inverter of the VRF system. In someembodiments, some and/or all steps of process 2400 are performed bymotor efficiency controller 2300.

Process 2400 is shown to include obtaining training data describingoperational conditions of a motor of a VRF system and associatedefficiency values of the motor (step 2402). The training data can beobtained from a variety of training data sources such as, for example,cloud databases, local storage of a building, based on user input, viafeedback from equipment, etc. The training data can include anyinformation relevant for training an AI model to predict efficiencyvalues of the motor. For example, the training data may include valuesof rotational speed of the motor during operation, ambient temperatures,heating/cooling duties, a current provided to the motor, a voltageprovided to the motor, power provided to the motor, estimated efficiencyvalues of the motor, etc. In some embodiments, step 2402 is performed bytraining data collector 2310.

Process 2400 is shown to include training an artificial intelligence(AI) model for predicting efficiency of the motor based on the trainingdata (step 2404). The AI model can be trained to learn a correlationbetween a set of variables affecting operation of the motor andcorresponding efficiency values of the motor. The AI model trained instep 2404 may be any of a variety of different AI models. For example,the AI model may be a linear RNN for evaluating efficiency of the motor.In some embodiments, step 2404 is performed by model generator 2312.

Process 2400 is shown to include obtaining data associated withoperation of the motor (step 2406). The data obtained in step 2406 maybe based on the motor during actual operation when predictions of motorefficiency are desired. In particular, the data obtained in step 2406can include values of input variables to the AI model generated in step2404. As such, the data can include values of a rotational speed of themotor, an ambient temperature, input power to the motor, etc. In someembodiments, the data may be obtained from some and/or all of the samesources used to obtain the training data in step 2402. In someembodiments, step 2406 is performed by prediction generator 2314.

Process 2400 is shown to include using the AI model to predict anefficiency of the motor based on the obtained data (step 2408). In step2408, the data obtained in step 2406 can be passed to the AI model asinput to generate predictions regarding efficiency of the motor. In someembodiments, the predicted efficiency of the motor is provided as apercentage of maximum efficiency. In some embodiments, the predictedefficiency is provided as another metric (e.g., a rating of “good,”“medium,” or “poor”). In some embodiments, step 2408 is performed byprediction generator 2314.

Process 2400 is shown to include determining a corrective action basedon the predicted motor efficiency outputted by the AI model (step 2410).The corrective action can be determined based on an estimateddegradation state of the motor. For example, a high efficiency value(e.g., >80% efficiency, >90% efficiency, etc.) may indicate the motor isnot significantly degraded. Accordingly, the corrective actiondetermined in step 2410 may be a corrective action associated with a lowcost (e.g., monetary cost, time cost, etc.) such as dispatching anotification to a user device or logging the estimated efficiency valueto a database. However, if the estimated efficiency is low (e.g., <50%efficiency, <40% efficiency, etc.), the corrective action determined instep 2410 may be a corrective action associated with a higher cost suchas scheduling maintenance for the motor, disabling the motor, etc. Inthis way, the corrective action determined in step 2410 can bedynamically adjusted based on the estimated efficiency of the motor. Insome embodiments, step 2410 is performed by corrective action generator2316.

Process 2400 is shown to include initiating the corrective action (step2412). Based on the corrective action determined in step 2410, step 2412can include determining how to initiate the corrective action andexecuting the steps necessary for the corrective action to be completed.For example, if the corrective action is notifying a user, step 2412 mayinclude generating a notification and communicating the notificationusing a communications interface. As another example, if the correctiveaction is disabling the motor, step 2412 may include generating acontrol signal that turns off the motor and providing the control signalto the motor. In some embodiments, step 2412 is performed by correctiveaction generator 2316.

Examples of Neural Network Implementations

Referring generally to FIGS. 25A and 25B, illustrations associated withAI model structures that can be for generating predictions are shown,according to some embodiments. The AI model structures described belowcan be utilized in any of the various controllers and/or methodsdescribed herein. For example, the AI model structures provided in FIGS.25A-25B may be utilized by compressor vibration controller 1310, VRFfault controller 2000, etc. It should be appreciated that the AI modelstructures provided in FIGS. 25A and 25B are provided purely for sake ofexample and is not intended to be limiting on AI model structures thatcan be utilized for generating predictions.

Referring now to FIG. 25A, an illustration of an RNN structure 2500 isshown, according to some embodiments. In some embodiments, RNN structure2500 is similar to and/or the same as RNN structure 900 as describedwith reference to FIG. 9A. In some embodiments, RNN structure 2500 mayillustrate a high-level view of an LSTM model. LSTM models are aspecific artificial RNN architecture that can be used in the field ofdeep learning. LSTM models can classify and process entire sequences oftime-series data and make predictions. Advantageously, LSTM models cangenerate prediction even with lags of unknown duration between importantevents in the time-series data. An LSTM model can include various layerssuch as, for example, a sequence input layer, one or more drop outlayers, one or more fully connected layers, one or more LSTM layers, anoutput layer, etc.

As shown in FIG. 25A, RNN structure 2500 can be represented by both acondensed model structure and an “unfolded” model structure. Theunfolded model structure can illustrate in greater detail how RNNstructure 2500 saves information over time which can affect outputs asinformation is not completely lost between time steps. In FIG. 25A, xcan represent an input to the RNN, U can represent input parametersapplied to x, o can represent an output of the RNN, W can representoutput parameters applied to an output of h to generate o, h canrepresent a primary block of the RNN that includes weights andactivation functions of the RNN, and v can represent informationcommunicated between time steps.

Referring now to FIG. 25B, an illustration of an LSTM model structure2550 is shown, according to some embodiments. LSTM model structure 2550can illustrate how information is saved between time steps in an RNN.LSTM model structure 2550 is shown to include functions f , g, i, and owhich can be used to generate outputs to the block shown in FIG. 25B.LSTM model structure 2550 is further shown to include a forget gate, anupdate gate, and an output gate. The forget gate can be configured toeliminate non-relevant data from being considered and remembered forfuture time steps in a time sequence. The update gate can apply someoperation to combine input information to account for changes in thedata. Finally, the output gate can decide what information is passed asoutput to the next time step. LSTM model structure 2550 can includemultiple blocks that pass information associated with a particular timestep in a time sequence to the next time step. Advantageously, thisstructure allows information to be retained and not lost between timesteps, thereby increasing an accuracy of prediction for time-seriesdata.

Configuration of Exemplary Embodiments

The construction and arrangement of the systems and methods as shown inthe various exemplary embodiments are illustrative only. Although only afew embodiments have been described in detail in this disclosure, manymodifications are possible (e.g., variations in sizes, dimensions,structures, shapes and proportions of the various elements, values ofparameters, mounting arrangements, use of materials, colors,orientations, etc.). For example, the position of elements can bereversed or otherwise varied and the nature or number of discreteelements or positions can be altered or varied. Accordingly, all suchmodifications are intended to be included within the scope of thepresent disclosure. The order or sequence of any process or method stepscan be varied or re-sequenced according to alternative embodiments.Other substitutions, modifications, changes, and omissions can be madein the design, operating conditions and arrangement of the exemplaryembodiments without departing from the scope of the present disclosure.

The present disclosure contemplates methods, systems and programproducts on any machine-readable media for accomplishing variousoperations. The embodiments of the present disclosure can be implementedusing existing computer processors, or by a special purpose computerprocessor for an appropriate system, incorporated for this or anotherpurpose, or by a hardwired system. Embodiments within the scope of thepresent disclosure include program products comprising machine-readablemedia for carrying or having machine-executable instructions or datastructures stored thereon. Such machine-readable media can be anyavailable media that can be accessed by a general purpose or specialpurpose computer or other machine with a processor. By way of example,such machine-readable media can comprise RAM, ROM, EPROM, EEPROM, CD-ROMor other optical disk storage, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to carry or storedesired program code in the form of machine-executable instructions ordata structures and which can be accessed by a general purpose orspecial purpose computer or other machine with a processor. Combinationsof the above are also included within the scope of machine-readablemedia. Machine-executable instructions include, for example,instructions and data which cause a general purpose computer, specialpurpose computer, or special purpose processing machines to perform acertain function or group of functions.

Although the figures show a specific order of method steps, the order ofthe steps may differ from what is depicted. Also two or more steps canbe performed concurrently or with partial concurrence. Such variationwill depend on the software and hardware systems chosen and on designerchoice. All such variations are within the scope of the disclosure.Likewise, software implementations could be accomplished with standardprogramming techniques with rule based logic and other logic toaccomplish the various connection steps, processing steps, comparisonsteps and decision steps.

1-40. (canceled)
 41. A controller for predicting faults in a heating,ventilation, or air conditioning (HVAC) system, the controllercomprising a processing circuit configured to: analyze operating datafor the HVAC system using a machine learning model to predict a faultclassification for the HVAC system, the fault classification identifyinga fault condition affecting the HVAC system; identify a HVAC device ofthe HVAC system associated with the fault condition; and automaticallyinitiate a corrective action to address the fault condition responsiveto identifying the HVAC device and the fault condition.
 42. Thecontroller of claim 41, wherein: the fault classification includes aseverity metric associated with the fault condition, the severity metricindicating a degree of influence that the fault condition has on theHVAC system; and the corrective action is determined based on both thefault condition and the severity metric associated with the faultcondition.
 43. The controller of claim 42, wherein the processingcircuit is configured to: automatically initiate a first correctiveaction in response to a value of the severity metric being below aseverity threshold; and automatically initiate a second correctiveaction in response to the value of the severity metric being above theseverity threshold.
 44. The controller of claim 43, wherein: the firstcorrective action comprises providing a notification to a user device;and the second corrective action comprises scheduling maintenance forthe HVAC system or replacement of the HVAC device associated with thefault condition.
 45. The controller of claim 42, wherein the correctiveaction comprises taking no action in response to a value of the severitymetric being below a severity threshold.
 46. The controller of claim 41,wherein the machine learning model is a recurrent neural network (RNN)model; and analyzing the operating data comprises providing a timeseries of values of the operating data as an input to the RNN model andobtaining a prediction of the fault classification as an output of theRNN model.
 47. The controller of claim 41, the processing circuitfurther configured to generate the machine learning model using a set ofsimulated training data obtained from a simulation model of the HVACsystem.
 48. The controller of claim 41, wherein the fault classificationidentifies a plurality of fault conditions affecting the HVAC system,the plurality of fault conditions associated with a plurality of HVACdevices of the HVAC system.
 49. The controller of claim 41, wherein thefault condition comprises at least one of: leakage of a refrigerant;frosting of an outdoor unit; clogging of an indoor fan; clogging of anindoor filter; clogging of a heat exchanger; clogging of an outdoor fan;demagnetization of a motor; or leakage of oil from a compressor.
 50. Thecontroller of claim 41, wherein the machine learning model is a firstmachine learning model and the processing circuit is configured to: usethe first machine learning model to predict the fault classification forthe HVAC system; and use a second machine learning model to predict aseverity of the fault condition identified by the fault classification.51. A method for predicting faults in a heating, ventilation, or airconditioning (HVAC) system, the method comprising: analyzing operatingdata for the HVAC system using a machine learning model to predict afault classification for the HVAC system, the fault classificationidentifying a fault condition affecting the HVAC system; identifying aHVAC device of the HVAC system associated with the fault condition; andautomatically initiating a corrective action to address the faultcondition responsive to identifying the HVAC device and the faultcondition.
 52. The method of claim 51, wherein: the fault classificationincludes a severity metric associated with the fault condition, theseverity metric indicating a degree of influence that the faultcondition has on the HVAC system; and the corrective action isdetermined based on both the fault condition and the severity metricassociated with the fault condition.
 53. The method of claim 52,comprising: automatically initiating a first corrective action inresponse to a value of the severity metric being below a severitythreshold; and automatically initiating a second corrective action inresponse to the value of the severity metric being above the severitythreshold.
 54. The method of claim 53, wherein: the first correctiveaction comprises providing a notification to a user device; and thesecond corrective action comprises scheduling maintenance for the HVACsystem or replacement of the HVAC device associated with the faultcondition.
 55. The method of claim 52, wherein the corrective actioncomprises taking no action in response to a value of the severity metricbeing below a severity threshold.
 56. The method of claim 51, whereinthe machine learning model is a recurrent neural network (RNN) model;and analyzing the operating data comprises providing a time series ofvalues of the operating data as an input to the RNN model and obtaininga prediction of the fault classification as an output of the RNN model.57. The method of claim 51, comprising generating the machine learningmodel using a set of simulated training data obtained from a simulationmodel of the HVAC system.
 58. The method of claim 51, wherein the faultclassification identifies a plurality of fault conditions affecting theHVAC system, the plurality of fault conditions associated with aplurality of HVAC devices of the HVAC system.
 59. The method of claim51, wherein the fault condition comprises at least one of: leakage of arefrigerant; frosting of an outdoor unit; clogging of an indoor fan;clogging of an indoor filter; clogging of a heat exchanger; clogging ofan outdoor fan; demagnetization of a motor; or leakage of oil from acompressor.
 60. The method of claim 51, wherein the machine learningmodel is a first machine learning model and the method comprises: usingthe first machine learning model to predict the fault classification forthe HVAC system; and using a second machine learning model to predict aseverity of the fault condition identified by the fault classification.